margaretmf/drug-spending

drug-spending

Slack: #drug-spending

Project Leads: @mattgawarecki, @jenniferthompson

Maintainers (people with commit access): TBD

Project Description: At its heart, this project seeks to gain a deeper understanding of where and how Medicare tax dollars are being spent. Healthcare is an increasingly important issue for many Americans; the Centers for Medicare and Medicaid Services estimate over 41 million Americans were enrolled in Medicare prescription drug coverage programs as of October 2016.

Because healthcare spending is a very real concern, we want to make it real -- not just for people who like reading graphs and looking at statistics, but for everybody. We're harnessing the power of data and modern computing to find answers to the questions people keep asking, and to make those answers easily understandable for anyone who wants to know more; questions like:

• Which conditions are we spending the most to treat?
• How much are people paying out of their own pockets for prescription drugs?
• What could Medicare and the American people do to save money, while also ensuring the same quality of care?

In conducting this research, we hope to gain new insights and create a positive impact for healthcare consumers and providers across the United States.

Getting started

Things you should know about

• "First-timers" are welcome! Whether you're trying to learn data science, hone your coding skills, or get started collaborating over the web, we're happy to help. (Sidenote: with respect to Git and GitHub specifically, our github-playground repo and the #github-help Slack channel are good places to start.)
• We've got (GitHub) Issues. Ready to dive in and do some good? Check out our issues board. Issues are how we officially keep track of the work we're doing, what we've done, and what we'd like to do next. You can identify different issue types by their tags:
  • first-pr: smaller issues to cut your teeth on as a first-time contributor
  • beginner-friendly: issues suitable for those with less experience or in need of mentorship
• We believe good code is reviewed code. All commits to this repository are approved by project maintainers and/or leads (listed above). The goal here is not to criticize or judge your abilities! Rather, sharing insights and achievements this way ensures that we all continue to learn and grow. Code reviews help us continually refine the project's scope and direction, as well as encourage the discussion we need for it to thrive.
• This README belongs to everyone. If we've missed some crucial information or left anything unclear, edit this document and submit a pull request. We welcome the feedback! Up-to-date documentation is critical to what we do, and changes like this are a great way to make your first contribution to the project.

Currently utilized skills

Take a look at this list to get an idea of the tools and knowledge we're leveraging. If you're good with any of these, or if you'd like to get better at them, this might be a good project to get involved with!

• Python 3 (scripting, analysis, Jupyter notebooks, visualization)
• R (analysis, R Markdown notebooks, visualization)
• JavaScript (visualization)
• Data extraction/ETL
• Data cleaning
• Data analysis

FAQ and other useful info

Downloading this repository

To download the code and data inside this repository, you'll need Git. Once you've got the necessary tools, open a command prompt and run git clone https://github.com/data4democracy/drug-spending.git to start downloading your own working copy. Once the command finishes, you should see a new drug-spending directory in the current directory's file listing. That's where you'll find it!

Project structure (or, "how do I find thing?")

• Source code and notebooks: For now, you'll find these in the repository's root directory. This will probably change in the future as we accumulate more files.
• Data files: All data files are located in the /data directory. At the moment, there is no further hierarchy beyond this; again, as we accumulate more data, this is likely to change.

Performing data analysis

There are many ways to analyze the data in this repository, but "notebook" formats like Jupyter and R Markdown are the most common. The setup process for these tools is in-depth enough to be outside the scope of this README, so please refer to documentation at the aforementioned links if necessary. If something isn't working quite right for you, that's okay! Continue reading to see how you can reach out for assistance.

Talking to people/asking for help

If you have questions or you'd like to discuss something on your mind, reach out to us in the #drug-spending channel on Slack. Project leads and maintainers are available for troubleshooting, brainstorming, mentorship, and just about anything else you might need.

System requirements (suggested)

• Git (check out the github-playground repository if you need a good place to get accustomed)
• An analytical language of your choice (Python, R, Julia, etc.)
• Python 3 (for Jupyter/.ipynb notebook files)
• RStudio (for R Markdown/.Rmd notebook files)

Completed goals

• Retrieve and clean Medicare Part D spending data from CMS
• Retrieve a listing of codes that link drugs to their therapeutic use(s)

To be completed

• Link drugs with their therapeutic use(s): We know which drugs Medicare funding is helping to pay for, but we don't yet have a good idea of which conditions they're being used to treat. Linking these two data points will grant enormous insight into how the American public uses Medicare.
• More data: We could really use some more data to give our analyses some depth and relatability.
• Lots more ideas to come: Join us in #drug-spending on Slack to contribute your ideas!