Using Electronic Health Record Data to Assess Residents’ Clinical Performance in the Workplace: The Good, the Bad, and the Unthinkable
Bibliographic record
Abstract
PURPOSE: Novel approaches are required to meet assessment demands and cultivate authentic feedback in competency-based medical education. One potential source of data to help meet these demands is the electronic health record (EHR). However, the literature offers limited guidance regarding how EHR data could be used to support workplace teaching and learning. Furthermore, given its sheer volume and availability, there exists a risk of exploiting the educational potential of EHR data. This qualitative study examined how EHR data might be effectively integrated and used to support meaningful assessments of residents' clinical performance. METHOD: Following constructivist grounded theory, using both purposive and theoretical sampling, in 2016-2017 the authors conducted individual interviews with 11 clinical teaching faculty and 10 senior residents across 12 postgraduate specialties within the Schulich School of Medicine and Dentistry at Western University. Constant comparative inductive analysis was conducted. RESULTS: Analysis identified key issues related to affordances and challenges of using EHRs to assess resident performance. These include the nature of EHR data; the potential of using EHR data for assessment; and the dangers of using EHR data for assessment. Findings offer considerations for using EHR data to assess resident performance in appropriate and meaningful ways. CONCLUSIONS: EHR data have potential to support formative assessment practices and guide feedback discussions with residents, but evaluators must take context into account. The EHR was not designed with the purpose of assessing resident performance; therefore, adoption and use of these data for educational purposes require careful thought, consideration, and care.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.080 | 0.137 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".