Performance improvement of operating rooms at WHSC using simulation and optimization
Bibliographic record
Abstract
The operating room (OR) is one of the most demanding departments at Winnipeg Health Science Center (V/HSC) which is the main healthcare facility serving adult surgical patients in Manitoba, Northwestem Ontario, and Nunavut.The problems faced to the OR are the long waiting list of patients and the ineffrcient utilization of human resources and facilities.The OR needs to treat a large variety of patient types versatility and dynamically.A discrete event simulation tool is used for modeling the OR operation.The major work involving in this research includes data collection, simulation modeling, model validation and output analysis.The initial results have shown the simulation potential in the performance improvement of healthcare systems.Based on the simulation model, Tabu Search (TS) is used as the optimizer in the meta-heuristics optimization method.The TS algorithm is used in conjunction with the simulation model of WHSC to find the optimum number of some resources in OR department.The contribution of this research is the integration of simulation and optimization in the performance improvement of healthcare systems.Parts of the solution generated in this research have been recommended to the WHSC for the action of the performance improvement. AckmowledgementsFirst of all, I am deeply indebted to Dr. Peng who led me to the wonderful world of simulation of health care systems.His stimulating suggestions and encouragement helped me in all the time of research and writing of this thesis.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".