A simulation model for perioperative process improvement
Bibliographic record
Abstract
Operating rooms (ORs) are a hospital’s largest cost center and greatest source of revenue. Surgical delays and cancellations lead to staff dissatisfaction due to long working hours, patient anxiety from long wait time, and extra costs for staff overtime. A discrete event simulation was used to model the perioperative process in the general surgery service at Toronto General Hospital, aiming to reduce the number of surgical cancellations and thereby improve the overall process. This model considers emergency case interruptions with different levels of urgency and takes into account the availability of five types of post-surgical beds. The effects of three scenarios on the number of surgical cancellations were examined: (1) scheduling the surgeons based on their patients usage length of post-surgical beds, (2) sequencing surgical procedures by length and variance, and (3) increasing the number of post-surgical beds. The results indicate that scheduling the surgeons in a weekly schedule based on the patients’ average length of stay in the ward, sequencing surgeries in order of increasing length and variance, and adding beds to the surgical ward all reduced the number of surgical cancellations, both individually and collectively. The interactions of all of these scenarios were compared against the current system and against each other to provide a basis for future OR planning and scheduling.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".