Inspiration: To reduce the COVID-19 disease burden in the United States
What it does: Defines the COVID-19 patient population based on Medical and Pharmacy Claims in order to identify spikes in therapies prescribed to this population and underscore potential supply shortages.
How I built it: Utilizes a dataset of Mx and Rx claims pulled into Snowflake and analyzed via a Jupyter notebook.
Challenges I ran into: Lack of available contemporary data and misalignment of COVID-19 diagnosing/prescribing across geographic regions.
Accomplishments that I'm proud of: The team was able to see significant drug demand spike signal despite limited claims data from February and March 2020!
What I learned: Certain therapies for COVID-19 may be overprescribed due to increased media coverage, however these therapy demand spikes can be noticed shortly after and the proper entities can be alerted.
What's next for COVID-19 Drug Demand and Supply Curves: Building out a more robust model to track COVID-19 drug demand spikes in real-time.