Teaching Electronic Medical Record (EMR) Data Discipline to Clinical Trainees: A Canadian Pilot Study
Bibliographic record
Abstract
Evidence suggests that patients whose electronic medical records (EMR) are documented with high quality data obtain higher quality care. However, most clinicians do not routinely document with this data discipline, and to date how to instruct this has not been clear. In this study, the key components of such an educational session for Canadian family medicine trainees were determined, with a particular focus on improving trainee awareness of the importance of data discipline and their understanding of data consistency. We piloted our developed session in three academic teaching sites and found no statistically significant difference when comparing two instructional methods – exploratory case simulations versus didactic only. However, trainee performance in general was satisfactory, suggesting robust immediate learning regardless of teaching method. Trainees also confirmed a strong desire for education on data discipline. Going forward, the educational session can be improved, become embedded in curricula, and evaluated using real-world EMR data.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads 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".