Operating Room Managers’ Use of Integer Programming for Assigning Block Time to Surgical Groups: A Case Study
Bibliographic record
Abstract
UNLABELLED: A common problem at hospitals with fixed amounts of available operating room (OR) time (i.e., "block time") is determining an equitable method of distributing time to surgical groups. Typically, facilities determine a surgical group's share of available block time using formulas based on OR utilization, contribution margin, or some other performance metric. Once each group's share of time has been calculated, a method must be found for fitting each group's allocated OR time into the surgical master schedule. This involves assigning specific ORs on specific days of the week to specific surgical groups, usually with the objective of ensuring that the time assigned to each group is close to its target share. Unfortunately, the target allocated to a group is rarely expressible as a multiple of whole blocks. In this paper, we describe a hospital's experience using the mathematical technique of integer programming to solve the problem of developing a consistent schedule that minimizes the shortfall between each group's target and actual assignment of OR time. Schedule accuracy, the sum over all surgical groups of shortfalls divided by the total time available on the schedule, was 99.7% (SD 0.1%, n = 11). Simulations show the algorithm's accuracy can exceed 97% with > or =4 ORs. The method is a systematic and successful way to assign OR blocks to surgeons. IMPLICATIONS: At hospitals with a fixed budget of operating room (OR) time, integer programming can be used by OR managers to decide which surgical group is to be allocated which OR on which day(s) of the week. In this case study, we describe the successful application of integer programming to this task, and discuss the applicability of the results to other hospitals.
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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 | 0.005 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".