demand-driven-open-data / ddod-intake

"DDOD Intake" tracks DDOD Use Cases using GitHub issues. View the main DDOD site here
http://ddod.us
28 stars 11 forks source link

Aggregate machine-readable provider network directories and drug formularies into tabular format #56

Open dportnoy opened 8 years ago

dportnoy commented 8 years ago

Putting out a call to those interested in making an impact by contributing to public data projects... Looking for somebody to create a new public dataset (and accompanying source code).

Background

In November 2015, the Centers for Medicare & Medicaid Services (CMS) enacted a new regulatory requirement for health insurers who list plans on insurance marketplaces. They must now publish a machine-readable version of their provider network directory and drug formulary, publish it to a specified JSON standard, and update it at least monthly. This data has just recently become accessible to the public. Some of its uses can be found in the Bayes Impact hackathon "prompts" or in at least 7 DDOD use cases.

Challenge

While these newly available datasets can significantly benefit consumer health applications and be used in a range of healthcare analytics, the current format doesn't lend itself to doing so.

Write code that does the following:

  1. Crawls the URLs starting with the "Machine-readable URL PUF" seed file found on this page: https://www.cms.gov/CCIIO/Resources/Data-Resources/marketplace-puf.html.
    • (As of 3/1/2016, there are 636 plan URLs, 23,711 provider URLs and 1,662 formulary URLs.)
  2. Converts the data from the JSON schema (https://github.com/CMSgov/QHP-provider-formulary-APIs) into a tabular format with the same fields.
    • (There may be some challenges converting multiple independent array fields from JSON to a tabular row.)
  3. Aggregates the results into CSV or text delimited text files, such that we have files for: Plans, Providers, and Formularies.

Run the code and let us know where to find the resulting files. We should be able to find a good home for them, so that they enjoy widespread use.

If you can do this, you’ll be an official Open Data Hero! (Spandex optional.)