Effects of medical scribes on physician productivity in a Canadian emergency department: a pilot study
Bibliographic record
Abstract
<h3>Background:</h3> 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. <h3>Methods:</h3> 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 <i>t</i> test. <h3>Results:</h3> 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]; <i>p</i> = 0.006). <h3>Interpretation:</h3> 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 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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".