Cited by 22Innovations in Medical EducationArtificial Intelligence in Healthcare and EducationOnline Learning and Analytics

Navigating the Landscape of Precision Education: Insights From On-the-Ground Initiatives

Abstract

A central goal of precision education (PE) is efficiently delivering the right educational intervention to the right learner at the right time. This can be achieved through a PE cycle that involves gathering inputs, using analytics to generate insights, planning and implementing interventions, learning and assessing outcomes, and then using lessons learned to inform modifications to the cycle. In this paper, the authors describe 3 PE initiatives utilizing this cycle. The Graduate Medical Education Laboratory (GEL) uses longitudinal data on graduate trainee behavior, clinical skills, and wellness to improve clinical performance and professional fulfillment. The Transition to Residency Advantage (TRA) program uses learner data from medical school coupled with individualized coaching to improve the transition to residency. The Anesthesia Research Group for Educational Technology (TARGET) is developing an automated tool to deliver individualized education to anesthesia residents based on a longitudinal digital representation of the learner. The authors discuss strengths of the PE cycle and transferrable learnings for future PE innovations. Common challenges are identified, including related to data (e.g., volume, variety, sharing across institutions, using the electronic health record), analytics (e.g., validating augmented intelligence models), and interventions (e.g., scaling up learner assessments with limited resources). PE developers need to share their experiences in order to overcome these challenges, develop best practices, and ensure ethical development of future systems. Adapting a common framework to develop and assess PE initiatives will lead to a clearer understanding of their impact, help to mitigate potential risks, and allow deployment of successful practices on a larger scale.

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Crossref · 10.1097/acm.0000000000005606

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Authors
Garibaldi, B. T., Hollon, M. M., Woodworth, G. E., Winkel, A. F., & Desai, S. V.
Affiliations
  • Johns Hopkins University (US)
  • Emory University (US)
  • Oregon Health & Science University (US)
  • NYU Langone Health (US)
  • American Medical Association (US)
Corresponding author
Garibaldi, Brian T
Venue
Academic Medicine
Published
2023-12-18
Volume
99
Issue
Supplement_1
Pages
S71-S76
ISSN
  • 1040-2446 (ISSN-L)
  • 1938-808X
OpenAlex
W4389958399
Cited by
22
References
31
Citations by year
2026: 9 · 2025: 8 · 2024: 3 · 2023: 2
FWCI
5.65
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
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
precision education, graduate medical education, transition to residency, personalized coaching, learning analytics, electronic health record data, augmented intelligence, student assessment, educational technology
MeSH terms
Mentoring, Education, Medical, Graduate, Humans, Internship and Residency

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