Precision Medical Education
Abstract
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.
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AI could potentially expedite students' proficiency and entrustability achievement, enabling time-variable graduation into the health care workforce.
“These skills are essential for medical trainees before residency and AI could potentially expedite students’ proficiency and entrustability achievement, enabling time-variable graduation into the health care workforce [20-23].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [23]
Deep learning programs that use data from medical records can provide learning opportunities through customized feedback on individual practices.
“In addition, deep learning programs that use data from medical records can identify cost of care variations [32,33] and provide learning opportunities through customized feedback on individual practices [23].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [23]
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- Authors
- Triola, M. M., & Burk-Rafel, J.
- Affiliations
- New York University (US)
- 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 ; Jesse.Rafel@nyulangone.org
- Corresponding author
- Triola, Marc M
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2023-04-03
- Volume
- 98
- Issue
- 7
- Pages
- 775-781
- ISSN
- 1040-2446 (ISSN-L)
- 1938-808X
- OpenAlex
- W4362692883
- Cited by
- 111
- References
- 59
- Citations by year
- 2026: 42 · 2025: 34 · 2024: 23 · 2023: 12
- FWCI
- 28.31
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Academic Medicine (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Health Professions › General Health Professions › Health and Medical Education
- Keywords
- personalized learning, competency-based medical education, educational analytics, artificial intelligence in medical education, decision support system, residency training, medical education, student outcomes
- MeSH terms
- Artificial Intelligence, Clinical Competence, Curriculum, Education, Medical, Humans, Internship and Residency, Learning
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