TRainee Attributable & Automatable Care Evaluations in Real-time (TRACERs): A Scalable Approach for Linking Education to Patient Care
Open accessAbstract
Competency-based medical education (CBME) is an outcomes-based approach to education and assessment that focuses on what competencies trainees need to learn in order to provide effective patient care. Despite this goal of providing quality patient care, trainees rarely receive measures of their clinical performance. This is problematic because defining a trainee’s learning progression requires measuring their clinical performance. Traditional clinical performance measures (CPMs) are often met with skepticism from trainees given their poor individual-level attribution. Resident-sensitive quality measures (RSQMs) are attributable to individuals, but lack the expeditiousness needed to deliver timely feedback and can be difficult to automate at scale across programs. In this eye opener, the authors present a conceptual framework for a new type of measure – TRainee Attributable & Automatable Care Evaluations in Real-time (TRACERs) – attuned to both automation and trainee attribution as the next evolutionary step in linking education to patient care. TRACERs have five defining characteristics: meaningful (for patient care and trainees), attributable (sufficiently to the trainee of interest), automatable (minimal human input once fully implemented), scalable (across electronic health records [EHRs] and training environments), and real-time (amenable to formative educational feedback loops). Ideally, TRACERs optimize all five characteristics to the greatest degree possible. TRACERs are uniquely focused on measures of clinical performance that are captured in the EHR, whether routinely collected or generated using sophisticated analytics, and are intended to complement (not replace) other sources of assessment data. TRACERs have the potential to contribute to a national system of high-density, trainee-attributable, patient-centered outcome measures.
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Projects are currently being piloted at New York University as part of the AMA's Reimagining Residency Grant where AI programs analyze all resident clinical notes and provide feedback, educational resources, and other suggestions based on their patient panel.
“Projects such as this are currently being piloted at New York University as part of the AMA’s Reimagining Residency Grant where AI programs analyze all resident clinical notes and provide feedback, educational resources, and other suggestions based on their patient panel [50].”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [50]
Residents receive daily messages with suggested readings based on the differential diagnoses that they encountered the day before.
“Residents receive daily messages with suggested readings based on the differential diagnoses that they encountered the day before.”
From Reimagining Core Entrustable Professional Activities for Undergraduate Medical Education in the Era of Artificial Intelligence (2023) · cites this paper as [50]
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Self-citationYang, J. (2025) · International Conference on Mechanical Automation and Engineering Applications (ICMAEA) · doi:10.1109/icmaea64564.2025.11725548
Leveraging AI to Accelerate Competency-Based Education in the Health Professions
Lomis, K. D., Pusic, M. V., Warm, E. J., Busari, J. O., Huwendiek, S., Incoll, I. W., Lau, S. T., Boscardin, C., Chen, C., Tanaka, P. P., et al. (2026) · Perspectives on Medical Education · doi:10.5334/pme.2594
Self-citation
- Authors
- Burk-Rafel, J., Sebok-Syer, S. S., Santen, S. A., Jiang, J., Caretta-Weyer, H. A., Iturrate, E., Kelleher, M., Warm, E. J., Schumacher, D. J., & Kinnear, B.
- Affiliations
- University of Cincinnati (US)
- University of California, Los Angeles (US)
- NYU Langone Health (US)
- Cincinnati Children's Hospital Medical Center (US)
- Corresponding author
- Santen, Sally A.
- Jiang, Joshua
- Iturrate, Eduardo
- Warm, Eric J.
- Schumacher, Daniel J.
- Kinnear, Benjamin
- ORCID
- Venue
- Perspectives on Medical Education
- Publisher
- Ubiquity Press, Ltd.
- Published
- 2023
- Volume
- 12
- Issue
- 1
- Pages
- 149-159
- ISSN
- 2212-2761 (ISSN-L)
- 2212-277X
- PMC
- PMC10198229
- OpenAlex
- W4377084984
- Cited by
- 38
- References
- 51
- Citations by year
- 2026: 7 · 2025: 9 · 2024: 10 · 2023: 12
- FWCI
- 9.73
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- cc-by
- Locations
- Perspectives on Medical Education (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University of Cincinnati
- Santen
- College of Medicine, University of Cincinnati
- Topics
- Health Sciences › Medicine › Public Health, Environmental and Occupational Health › Innovations in Medical Education
- Health Sciences › Medicine › Emergency Medicine › Hospital Admissions and Outcomes
- Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
- Keywords
- competency-based medical education, clinical performance measures, automated performance evaluation, real-time feedback, electronic health records, formative feedback, patient-centered outcome measures, medical residents
- MeSH terms
- Education, Medical, Graduate, Educational Measurement, Feedback, Humans, Internship and Residency, Learning
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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