Optimizing Surgical Capacity for a Rural Hospital Through Monte Carlo Simulation
Bibliographic record
Abstract
This article describes the application of Monte Carlo simulation to evaluate operating room (OR) utilization at a small rural hospital in the province of Ontario, Canada. Using input factors that include the duration of procedures, the number of days of operation, and the shift duration, the simulation identifies ways of improving the capacity of the hospital's OR. The analysis was used to implement changes that led to a 38% increase in the number of procedures completed. The hospital also has created capacity for further growth and is adding both new service types and additional revenue-generating contract services. Moreover, based on the success of the analytic model, hospital staff have continued collecting real-time data on OR utilization to enable additional analysis. The findings suggest that rural hospitals can indeed apply currently available data to improve process flow. Furthermore, the lack of analytic resources inside the hospital should not be a barrier because it is possible to create partnerships with educational institutions.
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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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".