A Review on the Contribution of Emergency Department Simulation Studies in Reducing Wait Time
Bibliographic record
Abstract
Background: Because of the important role of hospital emergency departments (EDs) in providing urgent care, EDs face a constantly large demand that often results in long wait times. Objective: To review and analyze the existing literature in ED simulation modeling and its contribution in reducing patient wait time. Methods: A literature review was conducted on simulation modeling in EDs. Results: A total of 41 articles have met the inclusion criteria. The papers were categorized based on their motivations, modeling techniques, data collection processes, patient classification, recommendations, and implementation statuses. Real impact is seldom measured; only four papers (~10%) have reported the implementation of their recommended changes in the real world. Conclusion: The reported implementations contributed significantly to wait time reduction, but the proportion of simulation studies that are implemented is too low to conclude causality. Researchers should budget resources to implement their simulation recommendations in order to measure their impact on patient wait time.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 | 0.000 |
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".