Datathons in Medical Education: A Case Study and Best Practice Recommendations (Preprint)

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.