Vision: Our goverment has wealth of data available on HHS. Our vision is to utilize the available information and make meaningful analysis out of it. For example: By joining the the data sets of doctors and provider information we can get more insight on why a particular area has more frauds. As more efforts are being made to standarize data like doctor ratings we can further leverage and allow people to make a more informed decision about insurance providers.
Goal: Our goal is to allow government to make use of the available data to solve problems on resource management, budgeting and better planning. For example: This can help in population lifestyle equitability, human migration, urban planning and even more.
Problem: Determine availability of providers and quality of doctors. We solve this by combining the datasets and querying for "NPI Deactivation Reason Code" and number of providers through elastic search.
Notebooks ==> API server(AWS) ==> ElasticSearch(AWS)
- jupyter/
- A collecition of ipynb notebooks to render interesting visualizations
- api-server/
- NodeJS API server that provides a simple API that Jupyter notebooks call into.
- data-loader/
- scripts to parse and normalize CSV data and store to elasticsearch.
- Endpoints
- GET /providers-by-state?speciality=
- returns an aggregated report of providers grouped by states.
- can optionally be filtered by speciality, eg
speciality=Diabetes
- GET /providers-by-zip
- returns an aggregated report of providers grouped by Zip codes.
- Fraud occurences
- Plot a heatmap based on number of provider deactivations due to fraud.
- Provider distribution by state
- Plot a heatmap based on raw counts of providers across states.
- Provider distribution by zip
- Plot a heatmap based on raw counts of providers across zip code boundaries.
- NPPES provider data from cms.gov. URL
- ~4.8 million providers data.