Teaching medical decision making with Posit Cloud

Harvard’s Data Science for Medical Decision Making course uses Posit Cloud to conduct investigations and discover new associations between 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:

• What are the variables that are input into the model and why?
• What statistical techniques are used to estimate the risk scores?
• What happens if you adjust, or “tweak” the input variables and how might it influence a decision?

When Chirag Patel began leading this course it quickly became difficult to replicate his daily work on a student’s desktop. Chirag codes, writes papers, and deploys data tools like Shiny apps in R, so consequently, R and Posit’s tools have become an important part of his teaching. While R, the RStudio IDE, 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 the following:

• The version of R students are running
• The packages and versions they are using
• Maintaining their cloud instance for the course
• Ultimately, maintaining a synonymous experience for all students

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 Posit Cloud, Chirag quickly moved the entire course to it by copying his GitHub repositories directly into the workspace in Posit Cloud.

Chirag wrote a tutorial of his own, inspired by Posit’s educational; resources, and included medical examples from his 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.

In explaining his reasoning for choosing Posit, Chirag shared, “because it makes everything transparent and code-first. All of the analytic choices you make are clear to the instructor and the other students.” Reproducibility and a code-first methodology are essential. This is a central theme for our course. Biomedicine is facing a reproducibility crisis; therefore, code-first is vitally important. Using Posit Cloud, students can present a paper in class and show their R Markdown code executing in Posit Cloud. Co-Instructors are also able to work on course assignments together and deploy them quickly to students. A unified view and quick deployment with Posit Cloud is a massive win for instructors and students.

The Department of Biomedical Informatics (DBMI)(opens in a new tab) is one of 11 basic and social science departments of Harvard Medical School. The Master of Biomedical Informatics (MBI)(opens in a new tab) program at Harvard Medical School is designed to advance the use of biomedical data, information, and knowledge for scientific inquiry, problem-solving, and decision making. This program is created for students who are looking to develop skills in data science in the context of medicine and biological sciences to improve human health. Our students graduate with the computational, methodological, and data science skills to contribute to an ongoing revolution in biomedical discovery.