Accepted for/Published in: JMIR Medical Education
Date Submitted: Jun 24, 2024
Date Accepted: Feb 25, 2025
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Datathons in Medical Education: A Case Study and Best Practice Recommendations
ABSTRACT
Background:
As artificial intelligence and machine learning become increasingly influential in clinical practice, it is critical for future physicians to understand how such novel technologies will impact the delivery of patient care.
Objective:
We describe a trainee-led, multi-institutional datathon as an effective means of teaching key data science and machine learning skills to medical trainees. We offer key insights on the practical implementation of such datathons and analyze experiences gained and lessons learned for future datathon iterations.
Methods:
We detail a recent datathon organized by MDplus, a national student-run nonprofit consisting of over 3,000 medical trainee members and physician-innovators. To assess the efficacy of the datathon as an educational experience, a short opt-in post-datathon survey was sent to all registered participants. Survey responses were de-identified and anonymized before downstream analysis to assess the quality of datathon experiences and areas for future work.
Results:
Our virtual datathon was attended by approximately 200 medical trainees across the United States. A diverse array of medical specialty interests were represented amongst participants, with 44% of survey participants expressing an interest in Internal Medicine, 33% in Surgery, and 19% in Radiology. Participant skills in leveraging Python and R for analyzing medical datasets improved after the datathon, and survey respondents enjoyed participating in the datathon (average score: 4.23 / 5).
Conclusions:
The datathon proved to be an effective and cost-effective means of providing medical trainees the opportunity to collaborate on data-driven projects in healthcare. Participants agreed that the datathon improved their ability to generate clinically meaningful insights from data. Our results suggest that datathons can serve as valuable and effective educational experiences for medical trainees to become better skilled in leveraging data science and artificial intelligence for patient care.
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