Teaching Medical Decision Making with RStudio CloudChirag Patel / Associate Professor of Biomedical Informatics at Harvard Medical School
“Have you ever Googled a health-related question and been dumbfounded by the hits? Wondered what an ‘odds ratio’ for a genetic variant you inherited is? Explanations of why we are who we are, and what diseases we might get, and why some of us are at risk, are often unsatisfactory.“
Harvard’s Data Science for Medical Decision Making course uses RStudio Cloud to conduct investigations and discover new associations with disease and health. Students are introduced to statistical decision theory and how modern data science and machine learning approaches can help improve medical decision making.
The course takes students through critical readings in the field and gives them the experience of analyzing the data from those papers to understand how the data is processed and might impact medical decision making.
Taking heart disease risk scores for example:
When Chirag Patel began leading this course it quickly became difficult to replicate his own daily work on a student’s desktop. Chirag codes, writes papers, and deploys data tools like Shiny apps in R, so consequently R and RStudio have become an important part of his teaching. While R, the RStudio development environment, and Shiny are all open source and freely available, configuring these tools, and keeping them in sync across an entire class, presented significant challenges for Chirag.
Aside from teaching the course, Chirag also previously had to worry about:
This was a lot of overhead and took a significant amount of time to work through. These challenges were soon accelerated with the pandemic as handling remote logins was even more difficult.
In just a month after learning about RStudio Cloud, Chirag was able to quickly move the entire course to it, by copying his GitHub repos directly into the workspace in RStudio Cloud.
Chirag wrote his own tutorial, inspired by RStudio education resources, and included medical examples from his own group, so students had content to work with from the start. The ability to send workspaces via a link, the classroom feel of the assignments view, and built-in tutorials soon became very helpful for students.
The time and overhead needed to manage an environment quickly diminished. The team no longer had to worry about running and maintaining their own cloud instance - giving out logins, ensuring everything was up, and handling occasional crashes.
RStudio provides open source and enterprise-ready professional software for data science teams using R and Python.