“EMERGing” Electronic Health Record Data Metrics: Insights and Implications for Assessing Residents’ Clinical Performance in Emergency Medicine
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: Competency-based medical education requires that residents are provided with frequent opportunities to demonstrate competence as well as receive effective feedback about their clinical performance. To meet this goal, we investigated how data collected by the electronic health record (EHR) might be used to assess emergency medicine (EM) residents' independent and interdependent clinical performance and how such information could be represented in an EM resident report card. METHODS: Following constructivist grounded theory methodology, individual semistructured interviews were conducted in 2017 with 10 EM faculty and 11 EM residents across all 5 postgraduate years. In addition to open-ended questions, participants were presented with an emerging list of EM practice metrics and asked to comment on how valuable each would be in assessing resident performance. Additionally, we asked participants the extent to which each metric captured independent or interdependent performance. Data collection and analysis were iterative; analysis employed constant comparative inductive methods. RESULTS: Participants refined and eliminated metrics as well as added new metrics specific to the assessment of EM residents (e.g., time between signup and first orders). These clinical practice metrics based on data from our EHR database were organized along a spectrum of independent/interdependent performance. We conclude with discussions about the relationship among these metrics, issues in interpretation, and implications of using EHR for assessment purposes. CONCLUSIONS: Our findings document a systematic approach for developing EM resident assessments, based on EHR data, which incorporate the perspectives of both clinical faculty and residents. Our work has important implications for capturing residents' contributions to clinical performances and distinguishing between independent and interdependent metrics in collaborative workplace-based settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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 it