Precision Education: The Future of Lifelong Learning in Medicine

Sanjay V. Desai*, Jesse Burk-Rafel, Kimberly D. Lomis, Kelly Caverzagie, Judee Richardson, Celia Laird O'Brien, John Andrews, Kevin Heckman, David Henderson, Charles G. Prober, Carla M. Pugh, Scott D. Stern, Marc M. Triola, Sally A. Santen

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)S14-S20
JournalAcademic Medicine
Volume99
Issue number4
DOIs
StatePublished - Apr 1 2024

Funding

The essential feature of a PE system is that it uses data and learning analytics to enable and propel the continuous cycle of development of individuals, programs, and organizations. This article is part of a supplement titled The Next Era of Assessment: Advancing Precision Education for Learners to Ensure High-Quality, Equitable Care for Patients and is funded by the American Medical Association, University of Cincinnati College of Medicine, Institute for Innovations in Medical Education of NYU Grossman School of Medicine, and Stanford University School of Medicine Department of Surgery.

ASJC Scopus subject areas

  • Education

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