ChatFDA: Medical Records Risk Assessment
M Tran C Sun
In healthcare, the emphasis on patient safety and the minimization of medical
errors cannot be overstated. Despite concerted efforts, many healthcare
systems, especially in low-resource regions, still grapple with preventing
these errors effectively. This study explores a pioneering application aimed at
addressing this challenge by assisting caregivers in gauging potential risks
derived from medical notes. The application leverages data from openFDA,
delivering real-time, actionable insights regarding prescriptions. Preliminary
analyses conducted on the MIMIC-III \cite{mimic} dataset affirm a proof of
concept highlighting a reduction in medical errors and an amplification in
patient safety. This tool holds promise for drastically enhancing healthcare
outcomes in settings with limited resources. To bolster reproducibility and
foster further research, the codebase underpinning our methodology is
accessible on
https://github.com/autonlab/2023.hackAuton/tree/main/prescription_checker. This
is a submission for the 30th HackAuton CMU.