Abstract Problem Undergraduate medical education (UME) often lacks detailed data on student learning in the clinical learning environment, instead relying on self-reported and observational assessments of student involvement in patient care. This reliance on subjective data can lead to inconsistencies and gaps in understanding student experiences during clinical encounters. The electronic health record (EHR) contains a wealth of data that could address these limitations but is underused in UME, limiting objective analysis of student encounters and hindering the ability to monitor and ensure consistent experiences across different clinical sites. Approach In 2020, a multidisciplinary team at the Kaiser Permanente Bernard J. Tyson School of Medicine used business intelligence software to develop dashboards that enhance analysis of student experiences in the clinical learning environment. Student encounters were identified using a unique EHR profile that enabled the capture of encounter-level data, which were then exported to a centralized dataset, facilitating creation of dashboards for comprehensive visualization and analysis of student experiences. Outcomes By 2024, 17 dashboards were created that included visit- and patient-specific data. The EHR-linked dashboards featured encounter-specific details (specialty, preceptor, visit type and specialty, chief concern, diagnoses) and patient-specific details (age, race, sex, language, interpreter use). This allowed the capture of student experiences and facilitated analysis of student quality and patient-reported experience metrics. The dashboards also served as feedback tools to ensure comparability between students and cohorts across clinical sites. Next Steps The dissemination of individualized student dashboards enables insights into clinical experiences and identifies student contributions to patient care. By sharing rich data, students can pinpoint learning opportunities and faculty can better support curricular goals, advancing precision medical education strategies. This approach can serve as a model for empirical studies on how clinical learning environments shape student development and marks a necessary step toward personalized learning systems in UME.
Silver et al. (Sat,) studied this question.