Developing and authenticating an electronic health record–based report card for assessing residents' clinical performance
Bibliographic record
Abstract
Purpose: The electronic health record (EHR) is frequently identified as a source of assessment data regarding residents' clinical performance. To better understand how to harness EHR data for education purposes, the authors developed and authenticated a prototype resident report card. This report card used EHR data exclusively and was authenticated with various stakeholders to understand individuals' reactions to and interpretations of EHR data when presented in this way. Methods: = 19) to develop and authenticate a prototype report card for residents. From February to September 2019, participants were invited to take part in a semistructured interview that explored their reactions to the prototype and provided insights about how they interpreted the EHR data. Results: Our results highlighted three themes: data representation, data value, and data literacy. Participants varied in terms of the best way to present the various EHR metrics and felt pertinent contextual information should be included. All participants agreed that the EHR data presented were valuable, but most had concerns about using it for assessment. Finally, participants had difficulties interpreting the data, suggesting that these data could be presented more intuitively and that residents and faculty may require additional training to fully appreciate these EHR data. Conclusions: This work demonstrated how EHR data could be used to assess residents' clinical performance, but it also identified areas that warrant further consideration, especially pertaining to data representation and subsequent interpretation. Providing residents and faculty with EHR data in a resident report card was viewed as most valuable when used to guide feedback and coaching conversations.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".