By: Kimberly Lomis, MD
One of the most rewarding aspects of my role as Vice President for Medical Education Innovations at the American Medical Association is the opportunity to provide tangible support to creative leaders in health professions education tackling our most challenging issues. (Keep reading to learn about an exciting new funding opportunity…)
Implementation of CBE with validity1 has proven to be one of those prickly issues. I have visited many educational programs –across countries, health professions and levels of training– striving to apply outcomes-based approaches. I have observed their struggle to support individualized pathways to competency. Programs often lack sufficient data about key elements of performance, particularly for domains beyond knowledge. Supervisors are overwhelmed with competing clinical demands, limiting the direct observation and feedback that is critical to programmatic assessment and engaging self-directed learners. What data is captured about performance is often buried in narrative comments within siloed evaluations. Educators are thus faced with a scarcity of information to guide individualized pathways, and even if a meaningful performance trend is identified, most educational programs lack the agility to provide tailored experiences to meet the learner’s developmental needs.
Precision education may position us to address these challenges. “A system that uses data and technology to transform lifelong learning by improving personalization, efficiency, and agency at the individual, program, and organization levels,”2 precision education strives to provide the right education to the right learner at the right time.3 In her blog on the topic last year, my friend Dr. Holly Caretta-Weyer proposed that precision education could “fuel the flywheel” for individualization. I like to describe precision education as CBE on the steroids of data and technology. In precision education systems (Figure 1), data inputs and analytics provide insights to inform customized interventions. This enables a master adaptive learner4 to engage in iterative cycles of planning, learning, assessing progress and adjusting practice as indicated by ongoing data input. (Visit Precision Education to explore resources and examples across the continuum of medical education)
Many educators have openly shared with me that such degree of precision is simply out of reach for their institutions, but emerging technologies such as artificial intelligence (AI) will aid us in pursuit of this vision. AI is enabling us to capture more data about performance. Natural language processing (NLP) is being used to mine the “data exhaust” of routine activities, such as clinical notes, for authentic performance insights. AI tools that reduce administrative burdens on clinical supervisors will facilitate more time for direct observation and targeted coaching. AI also helps make meaning of data; the capability of NLP to summarize narrative feedback from disparate sources reveals trends in performance, readily visualized on digital dashboards.
To address the difficulty of responding to individual learning needs with agility, AI offers new approaches. Learners I encounter in my travels are already using generative AI to drive multi-modal learning opportunities, easily transforming static curricular assets into versions that best fit individual learning preferences (such as converting a lecture into a podcast or quiz banks). Virtual simulation is being used to scale access to deliberate practice in key competencies as indicated by a learner’s individual needs. Predictive analytics can guide access to escalating clinical responsibilities, keeping each learner at their proximal zone of development throughout one’s career. Importantly, AI offers the opportunity to embrace more complex, inter-dependent variables, so we can research aspects of performance that we do not understand well, such as distributed cognition and teaming.
These are only a few examples of applications I witness emerging across our AMA partner programs, and the space is evolving rapidly.5 It is urgent that educators become knowledgeable about how these emerging technologies function.6 Awareness of limitations and consideration of how such tools perform in real-world workflows are necessary to ensure responsible development and deployment. Monitoring equity in access to, and the impacts of, precision education at both the individual and organizational levels must be a shared commitment of our entire educational community. And as excited as I am about technology, implementation of CBE with fidelity cannot be accomplished via technology alone. Current organizational cultures harbor counter-productive incentive structures, creating a risk that that clinicians at every level of professional development would be threatened by a sense of constant surveillance or the potential weaponization of data.7,8 The core principles of Learning Health Systems and deliberately developmental organizations9 must be fervently and jointly pursued by leaders of health systems and educational programs. Realizing the full potential of precision education will demand trusting learning relationships. A world with AI requires a shift in professional identity to strike the optimal balance of individual expertise, intellectual humility, and accountability. Leaders of educational environments must embrace their duty to nurture such identity formation.
The prospect of putting all this together feels daunting! Significant investment in the intentional and critical development of precision education systems is needed to bring our vision of competency-based education to reality. To this end, the American Medical Association is excited to announce a major initiative: Transforming Lifelong Learning through Precision Education. This $12 million grant program will support partnerships among leaders of medical education programs and experts in informatics and technology to build precision education systems. We seek projects designed to assess and elevate physician competencies that matter most in serving patients and communities, applying data and technology to personalize individual learner development. We intend to fund teams in a diversity of US settings and across the continuum of medical education. The AMA is eager to unleash the creativity of the medical education community to pave the way for future, broader implementation of precision education in support of CBE across health professions.
The views expressed in this post are those of the author and do not necessarily represent American Medical Association policy.
About the Author:
Kimberly Lomis, MD is Vice President for Medical Education Innovations at the American Medical Association, leading the AMA ChangeMedEd initiative as well as activities around artificial intelligence in medical education. Previously, Dr. Lomis served as professor of surgery and associate dean for UME at Vanderbilt University School of Medicine.
References
- Van Melle E et. al., International Competency-based Medical Education Collaborators. A Core Components Framework for Evaluating Implementation of Competency-Based Medical Education Programs. Acad Med. 2019 Jul;94(7):1002-1009.
- Desai et. al. Precision Education: The Future of Lifelong Learning in Medicine. Acad Med. 2024 Apr 1;99(4S Suppl 1):S14-S20.
- Triola & Burk-Rafel. Precision Medical Education. Academic Medicine 98(7):p 775-781, July 2023.
- Cutrer et. al. (2020). The Master Adaptive Learner. Philadelphia. Elsevier.
- Gordon, M., Daniel, M., Ajiboye, A., Uraiby, H., Xu, N. Y., Bartlett, R., … Thammasitboon, S. (2024). A scoping review of artificial intelligence in medical education: BEME Guide No. 84. Medical Teacher, 46(4), 446–470.
- Lomis et. al. Artificial Intelligence for Health Professions Educators. NAM Perspectives. Discussion Paper, National Academy of Medicine, Washington DC. 2021.
- Lomis et. al. The critical role of infrastructure and organizational culture in implementing competency-based education and individualized pathways in undergraduate medical education. Medical Teacher 2021. 43:sup2, S7-S16
- Ryan et. al. Competency-based medical education in a norm-referenced world: a root cause analysis of challenges to the competency-based paradigm in medical school. Acad Med 2023 Nov 1;98(11):1251-1260.
- Thoma et. al. (2021). Becoming a deliberately developmental organization: Using competency based assessment data for organizational development. Medical Teacher, 43(7), 801–809.
The views and opinions expressed in this post are those of the author(s) and do not necessarily reflect the official policy or position of The University of Ottawa . For more details on our site disclaimers, please see our ‘About’ page