Defining an optimal ED fast track strategy using simulation
Bibliographic record
Abstract
Purpose The Emergency Department (ED) at Grand River Hospital in Kitchener, Ontario sought insight into strategies that would reduce patient length of stay and queuing for initial assessment. The purpose of this paper is to focus on the ED's operational level and determine an optimal fast track strategy to improve performance measures. Design/methodology/approach The paper describes the application of discrete event simulation to the ED's “fast track” system and determines an optimal fast track strategy to improve performance measures. Topics discussed include: the design and development process for the simulation model, proposed operational strategies, and the analysis of scenarios for optimizing fast track. Findings Length of stay and queue length were most significantly reduced when there was an increased physician presence in the fast track system, followed by an additional emergency nurse practitioner in the system. Finally, the implementation of See‐and‐treat had a negligible effect on both performance measures for fast‐tracked patients. Originality/value Using real data, the effectiveness of a number of fast track strategies within a hospital ED were evaluated, which have practical implications for reducing patient wait times in ERs. This would be most valuable to practitioners in areas such as health service research, simulation modeling, and health service delivery.
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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 | 0.002 |
| 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".