Machine Learning to Predict-Then-Optimize Elective Orthopaedic Surgery Scheduling Improves Operating Room Utilization
Bibliographic record
Abstract
Abstract Objective To determine the potential for improving elective surgery scheduling for total knee and hip arthroplasty (TKA and THA, respectively) by utilizing a two-stage approach that incorporates machine learning (ML) prediction of the duration of surgery (DOS) with scheduling optimization. Materials and Methods Two ML models (for TKA and THA) were trained to predict DOS using patient factors based on 302,490 and 196,942 examples, respectively, from a large international database. Three optimization formulations based on varying surgeon flexibility were compared: Any: surgeons could operate in any operating room at any time, Split: limitation of two surgeons per operating room per day and MSSP: limit of one surgeon per operating room per day. Two years of daily scheduling simulations were performed for each optimization problem using ML-prediction or mean DOS over a range of schedule parameters. Constraints and resources were based on a high-volume arthroplasty hospital in Canada. Results The Any scheduling formulation performed significantly worse than the Split and MSSP formulations with respect to overtime and underutilization (p<0.001). The latter two problems performed similarly (p>0.05) over most schedule parameters. The ML-prediction schedules outperformed those generated using a mean DOS over all schedule parameters, with overtime reduced on average by 300 to 500 minutes per week. Using a 15-minute schedule granularity with a wait list pool of minimum 1 month generated the best schedules. Conclusion Assuming a full waiting list, optimizing an individual surgeon’s elective operating room time using an ML-assisted predict-then-optimize scheduling system improves overall operating room efficiency, significantly decreasing overtime. Highlights - A new approach for elective surgery scheduling was developed combining machine learning prediction of duration of surgery with integer linear programming optimization. - Models developed on six years of prospective multi-institution data and prediction results were used to generate two years of weekly simulated operating room schedules. - Three optimization models with varying levels of surgeon constraint were compared, and the model that optimized an individual surgeon’s wait list performed the best. - Using this approach reduced overtime by 300-500 minutes per week across five 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 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".