Bibliographic record
Abstract
Background/Purpose: Physician report cards are an important aspect of outcomes-based practice and education.Therefore, we investigated how data collected from the electronic health record (EHR) could assess emergency medicine (EM) residents' independent and interdependent clinical performance and how such information can be appropriately represented in an EM resident report card. Summary of the Innovation:Cerner EHR data were collected from 2017-2018, representing approximately 150,000 patient visits to the Emergency Department and 850 patient encounters, for one randomly selected fifth-year EM resident.A fifth-year resident was used for developing our prototype because we anticipated this performance level affords the best opportunity to look at both independent and interdependent metrics.Once we extracted all EHR data for our randomly selected resident, we developed an EM resident prototype report card that compared the resident's performance to faculty/other residents' performance.Our prototype breaks down EHR data by blocks, which better aligns with the rotation schedules of residents.Depending upon the metric (e.g.number of patients seen, bounce-back rates, or time to fluids/antibiotics), resident data were compared to faculty or year-appropriate residents'.Each metric was accompanied by an explanation of the metric and designation of independent or interdependent based on earlier interviews with faculty and residents.Finally, we present our development timeline and the resident prototype report card.Conclusion: Our findings document a process for developing resident report cards that incorporates the perspectives of clinical faculty and residents.This work has important implications for capturing residents' contributions to clinical performances by distinguishing between independent and interdependent performance.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.701 | 0.426 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".