ArticleJMIR medical education2026
Learning Analytics of a National Entrustable Professional Activities Platform: Cross-Sectional Study of System-Level Constraints on Advanced Entrustment in Competency-Based Medical Education.
Article in JMIR medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Dependability of Entrustable Professional Activity Portfolios in Otorhinolaryngology Residency: Nationwide Generalizability and Decision Study.JMIR medical education · 2026Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundCompetency-based medical education (CBME) relies on entrustable professional activities (EPAs) and Clinical Competency Committee (CCC) deliberation to support defensible decisions about trainee progression. As digital assessment platforms increasingly aggregate workplace-based assessment data across training programs, large-scale learning analytics can provide new insights into how entrustment decisions are generated and interpreted within CBME systems. However, little is known about how national assessment infrastructures influence patterns of entrustment attainment.
objectiveThis study examined national CCC summative entrustment decisions to identify system-level factors associated with attainment of expected supervision levels across residency training.
methodsWe conducted a cross-sectional analysis of nationwide CCC entrustment data derived from the Emyway digital assessment platform used by all accredited otolaryngology-head and neck surgery residency programs in Taiwan. The dataset included 3504 summative entrustment decisions across 12 EPAs for 292 residents. Observed supervision levels were compared with prespecified targets for each training year. Logistic regression models were used to identify factors associated with attainment of expected supervision levels, including training stage, EPA sequencing, CCC review cadence, and program characteristics.
resultsMedian supervision levels increased across training years but did not consistently reach expected targets in later stages. Expected entrustment targets were met in 2558 of 3504 decisions (73%). Underattainment was most pronounced in senior training years, particularly R4 and R5. In multivariable analyses, delayed EPA sequencing (adjusted odds ratio 0.75, 95% CI 0.63-0.91) and lower CCC review cadence (adjusted odds ratio 0.41, 95% CI 0.28-0.61) were independently associated with lower attainment. Program-level variability in entrustment outcomes was also observed.
conclusionsNationwide learning analytics of CCC entrustment decisions revealed a reproducible late-stage gap between expected and observed autonomy in CBME training. This pattern was associated with features of assessment system design that may be modifiable rather than trainee characteristics alone. National benchmarking of digital assessment data can therefore provide actionable insights to optimize EPA sequencing, CCC governance, and evidence generation within CBME systems.
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