The effects of publishing emergency department wait time on patient utilization patterns in a community with two emergency department sites: a retrospective, quasi-experiment design
Bibliographic record
Abstract
BACKGROUND: Providing emergency department (ED) wait time information to the public has been suggested as a mechanism to reduce lengthy ED wait times (by enabling patients to select the ED site with shorter wait time), but the effects of such a program have not been evaluated. We evaluated the effects of such a program in a community with two ED sites. METHODS: Descriptive statistics for wait times of the two sites before and after the publication of wait time information were used to evaluate the effects of the publication of wait time information on wait times. Multivariate logistical regression was used to test whether or not individual patients used published wait time to decide which site to visit. RESULTS: We found that the rates of wait times exceeding 4 h, and the 95th percentile of wait times in the two sites decreased after the publication of wait time information, even though the average wait times experienced a slight increase. We also found that after controlling for other factors, the site with shorter wait time had a higher likelihood of being selected after the publication of wait time information, but there was no such relationship before the publication. CONCLUSIONS: These findings were consistent with the hypothesis that the publication of wait time information leads to patients selecting the site with shorter wait time. While publishing ED wait time information did not improve average wait time, it reduced the rates of lengthy wait times.
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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 | 0.034 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".