HOW TO USE
- Upload an IMG, JPEG, or PNG file of Medical Bill📄
- Check the info tab for accurate data
- Submit to detect liklihood of fraud💬
- If any fraud is detected, submit details for a template email be made to send back to the doctor
WHAT IT DOES
MediGuard implements an unsupervised machine learning model in order to classify medical invoices with varying degrees of risk for fraud or error.
Our project starts by training a model and generating risk scores for various medical procedures and their corresponding prices. Then, users can upload their invoices to the website and our program will automatically extract important information such as the cost of the procedure. If needed, users have the ability to manually input corrections to this information as well.
Next, we compare the risk score using the data from the user's invoice and the initial risk score for the specified medical procedure. Upon comparing these two values, MediGuard will alert users about the likelihood that their medical invoice contains errors or fraud.
Finally, if fraud is detected, we utilize Google BARD to draft a template email to send to the doctor in order to make it easier for the patient to fix the error/fraud
HOW IT WORKS
In order to scan medical invoices, we used the Tesseract OCR (Optical Character Recognition) Engine along with openCV in order to take in an image of an invoice as user input and return the extracted information from this invoice.
Our machine learning model is an AutoEncoder that was trained on a data set consisting of information from over 100,000 medical professionals. This data set was made up of information including the service provided by the healthcare provider, the amount billed by the healthcare provider, the amount covered by Medicare, and more. For our purposes, we assume that most of the data in the set is "normal" when we trained the model and used these assumptions to identify anomalies in our data set. Using the anomaly scores generated by our model, we were able to identify anomalies in the data we used to test the model by comparing whether or not there was an increase in anomaly score. Based on the change in anomaly score and the initial anomaly score of the given procedure, we were able to classify fraud or errors as either being highly likely, likely, or not likely, providing users more insight about their medical invoice.
Our frontend was created using the Streamlit framework and gives users the option to upload a file containing the medical invoice that they want to detect errors or fraud in. Users also have the option to manually input any information that was incorrectly scanned. Then, based on the input provided by the user, we generate an anomaly score for the given invoice and report the fraud or error risk to the user
We use a UNOFFICIAL API for Google BARD and use it to ask it to draft a sample template based on the information processed.