Effect of a physician assistant on quality and efficiency metrics in an emergency department
Bibliographic record
Abstract
Abstract Objective To determine the effect of a physician assistant (PA) working in a secondary care hospital emergency department (ED) on the overall performance of the ED. Design A retrospective review of ED data from April 1, 2017, to September 30, 2017. Setting Belleville General Hospital, a secondary care hospital, ED in Ontario. Participants A physician assistant, 13 emergency physicians, and 7 family physicians. Main outcome measures Overall ED performance was evaluated using metrics from the Ontario Ministry of Health and Long-Term Care: rate of patients who left without being seen, provider initial assessment time at the 90th percentile, and the average provider initial assessment time for all patients over a 6-month period. Results In the PA group, there was a lower average daily left without being seen rate (3.4% vs 5.2%; P < .001), a lower provider initial assessment time at the 90th percentile (3.9 hours vs 4.5 hours; P < .001), a lower average provider initial assessment time (114.83 minutes vs 139.46 minutes; P < .001), and a lower average length of stay (313.85 minutes vs 348.91 minutes; P < .001). Conclusion This study suggests that a PA has a statistically significant positive effect on the overall performance of an ED. Future studies should examine the effect of a PA on quality of care and hospital funding.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".