Precision Education: The Future of Lifelong Learning in Medicine
Abstract
The goal of medical education is to produce a physician workforce capable of delivering high-quality equitable care to diverse patient populations and communities. To achieve this aim amidst explosive growth in medical knowledge and increasingly complex medical care, a system of personalized and continuous learning, assessment, and feedback for trainees and practicing physicians is urgently needed. In this perspective, the authors build on prior work to advance a conceptual framework for such a system: precision education (PE). PE is a system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels. PE “cycles” start with data inputs proactively gathered from new and existing sources, including assessments, educational activities, electronic medical records, patient care outcomes, and clinical practice patterns. Through technology-enabled analytics, insights are generated to drive precision interventions. At the individual level, such interventions include personalized just-in-time educational programming. Coaching is essential to provide feedback and increase learner participation and personalization. Outcomes are measured using assessment and evaluation of interventions at the individual, program, and organizational levels, with ongoing adjustment for repeated cycles of improvement. PE is rooted in patient, health system, and population data; promotes value-based care and health equity; and generates an adaptive learning culture. The authors suggest fundamental principles for PE, including promoting equity in structures and processes, learner agency, and integration with workflow (harmonization). Finally, the authors explore the immediate need to develop consensus-driven standards: rules of engagement between people, products, and entities that interact in these systems to ensure interoperability, data sharing, replicability, and scale of PE innovations.
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Empirical evidence regarding AI effectiveness in structured medical knowledge learning remains insufficient.
“Although AI technologies are increasingly introduced into higher education, empirical evidence regarding their effectiveness in structured medical knowledge learning remains insufficient [16, 17].”
Cites
The assessment of professional competence: Developments, research and practical implications
Van Der Vleuten, C. P. M. (1996) · Advances in Health Sciences Education · doi:10.1007/bf00596229 · Cited by 1,247 · see citing works
Demystifying AI: Current State and Future Role in Medical Education Assessment
Turner, L., Hashimoto, D. A., Vasisht, S., & Schaye, V. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005598 · Cited by 70 · see citing works
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Navigating the Landscape of Precision Education: Insights From On-the-Ground Initiatives
Garibaldi, B. T., Hollon, M. M., Woodworth, G. E., Winkel, A. F., & Desai, S. V. (2024) · Academic Medicine · doi:10.1097/acm.0000000000005606 · Cited by 22 · see citing works
Self-citationZhao, C., Zhu, J., Liu, J., Zhao, W., & Pang, Y. (2026) · BMC Medical Education · doi:10.1186/s12909-026-09469-0 · Cited by 1 · see citing works
“Although AI technologies are increasingly introduced into higher education, empirical evidence regarding their effectiveness in structured medical knowledge learning remains insufficient [16, 17].”
- Authors
- Desai, S. V., Burk-Rafel, J., Lomis, K. D., Caverzagie, K., Richardson, J., O’Brien, C. L., Andrews, J., Heckman, K., Henderson, D., Prober, C. G., Pugh, C. M., Stern, S. D., Triola, M. M., & Santen, S. A.
- Affiliations
- American Medical Association (US)
- is assistant professor, Division of Hospital Medicine, and assistant director of precision medical education, Institute for Innovations in Medical Education, NYU Grossman School of Medicine, New York, New York J. Burk-Rafel ; jesse.rafel@nyulangone.org
- University of Nebraska Medical Center (US)
- Northwestern University (US)
- Stanford Medicine (US)
- University of Chicago (US)
- New York University (US)
- Virginia Commonwealth University (US)
- Corresponding author
- Desai, Sanjay V
- ORCID
- Venue
- Academic Medicine
- Publisher
- Oxford University Press (OUP)
- Published
- 2024-01-29
- Volume
- 99
- Issue
- Supplement_1
- Pages
- S14-S20
- ISSN
- 1040-2446 (ISSN-L)
- 1938-808X
- OpenAlex
- W4391285534
- Cited by
- 81
- References
- 2
- Citations by year
- 2026: 39 · 2025: 25 · 2024: 10 · 2023: 7
- FWCI
- 30.65
- 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
- precision education, continuing professional development, learner agency, health equity, value-based care, interoperability standards, medical workforce
- MeSH terms
- Education, Continuing, Education, Medical, Educational Status, Humans, Learning, Medicine
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