Use of Linear Programming to Estimate Impact of Changes in a Hospital's Operating Room Time Allocation on Perioperative Variable Costs
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Bibliographic record
Abstract
BACKGROUND: Administrators at hospitals with a fixed annual budget may want to focus surgical services on priority areas to ensure its community receives the best health services possible. However, many hospitals lack the detailed managerial accounting data needed to ensure that such a change does not increase operating costs. The authors used a detailed hospital cost database to investigate by how much a change in allocations of operating room (OR) time among surgeons can increase perioperative variable costs. METHODS: The authors obtained financial data for all patients who underwent outpatient or same-day admit surgery during a year. Linear programming was used to determine by how much changing the mix of surgeons can increase total variable costs while maintaining the same total hours of OR time for elective cases. RESULTS: Changing OR allocations among surgeons without changing total OR hours allocated will likely increase perioperative variable costs by less than 34%. If, in addition, intensive care unit hours for elective surgical cases are not increased, hospital ward occupancy is capped, and implant use is tracked and capped, perioperative costs will likely increase by less than 10%. These four variables predict 97% of the variance in total variable costs. CONCLUSIONS: The authors showed that changing OR allocations among surgeons without changing total OR hours allocated can increase hospital perioperative variable costs by up to approximately one third. Thus, at hospitals with fixed or nearly fixed annual budgets, allocating OR time based on an OR-based statistic such as utilization can adversely affect the hospital financially. The OR manager can reduce the potential increase in costs by considering not just OR time, but also the resulting use of hospital beds and implants.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it