Operating Room Scheduling by Considering Surgical Inventory, Post Anesthesia Beds, and Emergency Surgeries to Improve Efficiency During the COVID-19 Outbreak with Machine Learning
Bibliographic record
Abstract
Surgical operating rooms are critical in the total hospital costs, while surgical care accounts for one-third of hospital costs. Thus, successful and improve operating room management and scheduling can bring significant benefits. In this study, we develop Operating Room (OR) scheduling problem integrated with a Post-Anesthesia Care Unit (PACU) by considering emergency surgeries during the COVID-19 outbreak. Accurate prediction of surgery duration and required PACU time for each surgery are critical for operating room scheduling. Due to the inherent uncertainty in surgery duration and PACU time, we develop supervised machine learning to estimate surgery duration and PACU time. Finally, based on discrete event simulation, we compare our proposed surgery scheduling model to the available scheduling by using statics and data from Montreal's hospitals. We could show that our scheduling model can significantly increase operating room utilization with the issue of PACU congestion.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".