Precision Medical Education

Bibliographic Data

ID21615096
AuthorsMarc M Triola (0000-0002-6303-3112, M.M. Triola is associate dean of educational informatics and director of the Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York, corresponding author), Jesse Burk-Rafel (0000-0003-3785-2154, J. Burk-Rafel is assistant director of precision and translational education, Institute for Innovations in Medical Education, and assistant professor of medicine, Division of Hospital Medicine, NYU Grossman School of Medicine, New York, New York ; [email protected])
Year2023
Volume98
Issue7
Pages775-781
Publication date2023-07-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueAcademic Medicine (JOURNAL)
Journal identifiersISSN: 1040-2446 • E-ISSN: 1938-808X
PublisherOxford University Press (OUP) (PUBLISHER)
DOI10.1097/acm.0000000000005227
PMID37027222
OpenAlexW4362692883
LanguageEN
Citations received20
References cited57

Medical schools and residency programs are increasingly incorporating personalization of content, pathways, and assessments to align with a competency-based model. Yet, such efforts face challenges involving large amounts of data, sometimes struggling to deliver insights in a timely fashion for trainees, coaches, and programs. In this article, the authors argue that the emerging paradigm of precision medical education (PME) may ameliorate some of these challenges. However, PME lacks a widely accepted definition and a shared model of guiding principles and capacities, limiting widespread adoption. The authors propose defining PME as a systematic approach that integrates longitudinal data and analytics to drive precise educational interventions that address each individual learner’s needs and goals in a continuous, timely, and cyclical fashion, ultimately improving meaningful educational, clinical, or system outcomes. Borrowing from precision medicine, they offer an adapted shared framework. In the P4 medical education framework, PME should (1) take a proactive approach to acquiring and using trainee data; (2) generate timely personalized insights through precision analytics (including artificial intelligence and decision-support tools); (3) design precision educational interventions (learning, assessment, coaching, pathways) in a participatory fashion, with trainees at the center as co-producers; and (4) ensure interventions are predictive of meaningful educational, professional, or clinical outcomes. Implementing PME will require new foundational capacities: flexible educational pathways and programs responsive to PME-guided dynamic and competency-based progression; comprehensive longitudinal data on trainees linked to educational and clinical outcomes; shared development of requisite technologies and analytics to effect educational decision-making; and a culture that embraces a precision approach, with research to gather validity evidence for this approach and development efforts targeting new skills needed by learners, coaches, and educational leaders. Anticipating pitfalls in the use of this approach will be important, as will ensuring it deepens, rather than replaces, the interaction of trainees and their coaches

Analytics · Coaching · Data science · Knowledge management · Medical education · Personalization · Psychological intervention · World Wide Web · Computer Science · Health and Medical Research Impacts · Innovations in Medical Education · Medical Education and Admissions · Medicine · Nursing · Psychology

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