Reducing elective general surgery cancellations at a Canadian hospital
Bibliographic record
Abstract
BACKGROUND: In Canadian hospitals, which are typically financed by global annual budgets, overuse of operating rooms is a financial risk that is frequently managed by cancelling elective surgical procedures. It is uncertain how different scheduling rules affect the rate of elective surgery cancellations. METHODS: We used discrete event simulation modelling to represent perioperative processes at a hospital in Toronto, Canada. We tested the effects of the following 3 scenarios on the number of surgical cancellations: scheduling surgeons' operating days based on their patients' average length of stay in hospital, sequencing surgical procedures by average duration and variance, and increasing the number of postsurgical ward beds. RESULTS: The number of elective cancellations was reduced by scheduling surgeons whose patients had shorter average lengths of stay in hospital earlier in the week, sequencing shorter surgeries and those with less variance in duration earlier in the day, and by adding up to 2 additional beds to the postsurgical ward. CONCLUSION: Discrete event simulation modelling can be used to develop strategies for improving efficiency in operating rooms.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".