Improving patient satisfaction by adding a physician in triage
Bibliographic record
Abstract
Background: The physician in triage (PIT) model has been proposed as a process improvement to help increase efficiency in the Emergency Department setting. However, its effect on patient satisfaction has not been well established. Methods: An interventional study comparing patient satisfaction scores for the 6-month period before and after implementation of a physician in triage model. In our system an additional attending physician was assigned to triage from 1 p.m. to 9 p.m. daily. Outcome measures were mean scores obtained from respondents to Press Ganey® patient satisfaction surveys for selected questions most likely to be impacted by PIT implementation and those included in the physician section of the survey. Results: Five hundred and eight respondents seen in the six months before the initiation of the PIT team and 458 respondents in the six months after the system change were included in the study. Improvement was noted in the absolute Press Ganey® scores in the Post-PIT time period across all questions analyzed with statistically significant differences noted for 8 of the 10 questions studied. Conclusions: Although seemingly small there was a statistically significant improvement in the absolute patient satisfaction scores after adding a physician in triage. Because small gains in absolute scores can result in large improve- ments on the percentile rank when using Press Ganey® surveys, physician in triage may be of significant benefit to overall patient satisfaction.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".