Performance Improvement of ED at VGH Using Simulation and Optimization
Bibliographic record
Abstract
Emergency department(ED) is one of the busiest clinical units in Winnipeg Victoria Gen-eral Hospital (VGH) which faces the challenge of patients’ long waiting-time as increas-ing healthcare demand and limited resources. This research investigates the critical factors of the ED operation to enhance the operational efficiency using simulation modeling and optimization. The contribution of this research is the integration of simulation and optimization for the performance improvement of ED operations. Discrete-events simula-tion (DES) methodology provides a cost-effective tool to analyse the performance of the ED operations and evaluates the potential alternatives. Design of experiments (DOE) and Scatter search (SS) of model optimization are proposed to search the ED potential capaci-ty for waiting-time reduction. The patient-flow is accelerated along with the waiting-time reduction, which results in better efficient patient throughput in the ED. A specific strate-gy is suggested to improve the ED operation based on the simulation model.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".