Medical malpractice cases are inherently difficult for many reasons. First, the attorney that handles the case is not trained to analyze medical data, practices and procedures. Hiring a doctor to review a complex case is cost prohibitive and may prove unproductive for a number of reasons such as professional curtesy, looking at the case from the doctor’s prospective biases the opinion and findings of the doctor, and due to similar training and practices sees nothing wrong with the quality of treatment (i.e., every doctor has to see 50 patients a day to make extra money so we cut corners, we all do it so I can’t blame Joe).

A data scientist with a background in biology and working on medical applications becoming familiar with HIPAA, SOAP Notes, etc. can provide the technical background necessary to make the necessary findings and develop a case while not having any of the bad habits and ties to the profession. Also, a data scientist is trained to examine the all the data and find the relationships. In many ways the training makes them perfect for this specific kind of analysis. Data scientists also are trained to be methodical and detailed.

In the case of the wonderful Ms. Migen Dibra, having a data science background makes one well trained to analyze and assess all of the medical studies given to the FDA for the drug “label” approvals, which can be most revealing.

This lecture will cover these important subjects as well as go through the case of Ms. Migen Dibra who died prematurely.