Effects of medical scribes on physician productivity in a Canadian emergency department: a pilot study
Bibliographic record
Abstract
Background: Emergency department efficiency is a priority across Canada. In the United States, scribes may increase the number of patients seen per hour per physician; however, Canadian data are lacking. We sought to implement scribes in a Canadian emergency department with the hypothesis that scribes would increase the number of patients seen per hour per physician. Methods: We conducted a 4-month quality improvement pilot study in a community emergency department in Ottawa, Ontario. Data collection began January 2015 after scribe training. Physicians received shifts with and without a scribe for a period of 4 months. Across the study, the mean number of patients seen per hour was determined for each physician during shifts with and without a scribe. We compared mean (± standard deviation [SD]) number of patients seen per hour based on presence or absence of a scribe by 2-tailed paired-samples t test. Results: Eleven scribes participated and ranged in age from 18 to 23 years. Twenty-two full- or part-time emergency physicians were followed. We documented 463 physician-hours without use of a scribe and 693.75 physician-hours with use of a scribe. Across all 22 physicians, 18 (81.8%) saw more patients per hour with use of a scribe. Overall, the number of patients seen per hour per physician was significantly greater (+12.9%) during shifts with a scribe (mean [± SD] 2.81 [± 0.78]) than during shifts without a scribe (mean [± SD] 2.49 [± 0.60]; p = 0.006). Interpretation: In this pilot study, the use of scribes resulted in an increased number of patients seen per hour per physician. Because this was a small study at a single centre, further research on the effects of scribes in Canada is warranted.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".