Conrado Garcia - Science hack-day project

Rationale

The user in this use-case is a health provider supply administrator.

After analysing the data provided in the dataset, I had the idea to present the data in a way that may provide some insight hard to see otherwise.

Each "record" of the dataset contains some geographical information, such as state, city and R.U.C.A (Rural-Urban Commuting Area codes).

With this parameters, the idea of finding the most common procedures performed in a given state, aggregated by cities which fall inside a R.U.C.A range.

This could give the "user" the lead on which procedures should be prioritized on certain zones and distribute supplies accordingly. For example: most common procedures in Rural Texas or the Metropolitan cities of New York.

Pre-requisites

1. Have docker-compose installed
2. Have the public dataset from Centers for Medicare and Medicaid Services (CSV file, must be named MUP_PHY_R21_P04_V10_D19_Prov_Svc.csv)

How to run for development

1. Git clone the repo
2. Make sure you're in a new virtualenv
3. Run pip install -r requirements-dev.txt
4. Run pre-commit install
5. Run pytest at the root of the project, all tests should pass
6. You're ready for development!

How to run for production

1. Git clone the repo
2. Move the CSV to app/data/MUP_PHY_R21_P04_V10_D19_Prov_Svc.csv
3. At the root of the repo, run docker-compose up --build -d
4. To load data, run docker-compose run api /code/load_data.sh
5. Wait for the data to load, 1 million records should be added to the database
6. In your browser, go to http://localhost:8000/docs
7. In the OpenAPI interface, select the /procedure/ endpoint and click "Try it out"
8. Try different values for state_abbreviation, min_ruca and max_ruca