By: Holly Caretta-Weyer (@holly_cw)
When I talk to colleagues around the world about implementing competency-based education (CBE), I often hear struggles related to obtaining sufficient assessment data, creating a coaching program, crafting individualized learning pathways, or defending decisions around progression. And each of these concerns varies depending upon the local context. Given the frequency and variability of these concerns, this raises the question of how are we to continue to operationalize and implement CBE in a meaningful and adaptable way across a variety of contexts where systems, structures, and competing priorities abound.
While the five core components of CBE were originally presented graphically with two (outcome competencies and sequenced progression) front and center, with the intent for constructive alignment of the remaining components (tailored learning experiences, competency-focused instruction, and programmatic assessment) into a balanced ecosystem,1 many have found this balance nearly impossible to attain. So much attention has been paid to designing programmatic assessment systems and obtaining data that the other components are often relegated to the back burner. Others have focused more on coaching for the attainment of competence but struggle to do so without sufficient assessment data.
Is a balanced system of each of the five components within a given context perhaps then not our ultimate goal, given the frequent difficulties in attaining such stability? Enter the concept of precision medical education.
Precision Medical Education Framework
Precision medical education is defined as “a systematic approach that integrates longitudinal data and analytics to drive precise educational interventions that address each individual learner’s needs and goals in a continuous, timely, and cyclical fashion, ultimately improving meaningful educational, clinical, or system outcomes.”2 Triola and Burk-Rafel subsequently propose a framework for precision medical education that consists of 4 “P”s:
- Take a proactive approach to acquiring and using learner data that is longitudinal and accounts for context, experiences, assessed competence, and individual learner needs and goals;
- Generate timely personalized insights through precision analytics including artificial intelligence, decision-support tools, and predictive modeling;
- Design precision education interventions including learning resources, assessment, coaching, and individualized pathways in a participatory fashion or using a co-production approach;
- Ensure interventions are predictive of meaningful educational, professional, or clinical/patient, or health systems outcomes.2
While some components of precision medical education still remain impractical for many who lack substantial data infrastructure to support components such as predictive analytics, much of this model is attainable through small, flexible changes within one’s educational context. As such, using the precision medical education model as an approach to fueling the implementation of the core components of CBE may unlock some of the answers to pushing forward with CBE in our often unbalanced and ever-evolving educational ecosystems.
Precision Medical Education as the Fuel for CBE
The precision medical education model provides guidance as to how to go about operationalizing a cyclic approach to create a flywheel effect to gather the right data for the right learner at the right time within a given context aligned with the ultimate outcomes in mind. This does not require balance but simply considering what the right approach is at that moment in order to fuel the flywheel toward attaining the desired outcomes at the end of the developmentally sequenced progression for each learner.
To operationalize this, first, consider what meaningful educational, professional, clinical/patient, or health systems outcomes you can measure. This should align with the outcomes competencies necessary for progression within a CBE paradigm. Second, determine what sequenced progression would look like within your context. This will guide the proactive approach to acquiring and using learner data as it will help to define the requisite experiences, assessed competence, and individual learner needs and goals you need to provide in order to gather the data you need to drive the flywheel. Third, gather your fuel. This is what you have worked for in designing your programmatic assessment. Each element fit for purpose should provide the data you identified in the outcomes to help guide you in determining sequenced progression. This does not have to be tech-forward at this point – use what you have. However, artificial intelligence support tool are becoming more ubiquitous, accessible, and affordable and will aid you in gathering personalized data over time to further fuel the flywheel. Finally, your output is the participatory or co-produced interventions using various learning resources, coaching, and individualized pathways. This will guide further assessment to further fuel the flywheel in a cyclic fashion as it climbs the developmental curve toward meeting the defined outcomes.
A program of assessment then becomes not a one-size-fits-all program but a tailored program, within some general boundary conditions, but different for all learners. As an example: for some learners frequent observation can appear very useful, while for others observing less frequently is enough (but “no observations” would never occur).
Thus, using precision medical education, we are able to fully operationalize the core components into a cyclic flywheel where, even when out of balance, we are able to continue to drive toward our ultimate outcomes. While this will take iterating as barriers are identified and systems modified, this approach should mitigate some of the current hurdles and pain points by providing an alternative view and generating small wins as the cycles repeat and initial outcomes are met for all stakeholders involved.
About the author: Holly Caretta-Weyer, MD, MHPE, is Associate Dean for Admissions and Director of Assessment for the Stanford University School of Medicine and Associate Professor and Director of Evaluation and Assessment in the Department of Emergency Medicine. She is also the Principal Investigator on a Reimagining Residency Grant from the American Medical Association seeking to implement CBE within emergency medicine in the United States. Dr.Caretta-Weyer’s program of research centers on the design, implementation, and intersection of competency-based assessment, selection, and summative entrustment decision-making processes.
References
- Van Melle E, Frank JR, Holmboe ES, et al. A Core Components Framework for Evaluating Implementation of Competency-Based Medical Education Programs. Acad Med. July 2019;94(7):1002-1009.
- Triola MM, Burk-Rafel J. Precision medical education. Acad Med. July 2023;98(7):775-781.
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