Precision Education: The Future of Lifelong Learning in Medicine.
Sanjay V DesaiJesse Burk-RafelKimberly D LomisKelly CaverzagieJudee RichardsonCelia Laird O'BrienJohn AndrewsKevin HeckmanDavid HendersonCharles G ProberCarla M PughScott D SternMarc M TriolaSally A SantenPublished in: Academic medicine : journal of the Association of American Medical Colleges (2024)
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.
Keyphrases
- healthcare
- electronic health record
- quality improvement
- big data
- physical activity
- primary care
- public health
- palliative care
- case report
- medical education
- emergency department
- social media
- health information
- mental health
- type diabetes
- high resolution
- drinking water
- adipose tissue
- climate change
- metabolic syndrome
- insulin resistance
- health promotion
- current status