Calculating a Potential Increase in Hospital Margin for Elective Surgery by Changing Operating Room Time Allocations or Increasing Nursing Staffing to Permit Completion of More Cases: A Case Study
Bibliographic record
Abstract
UNLABELLED: Administrators routinely seek to increase contribution margin (revenue minus variable costs) to better cover fixed costs, provide indigent care, and meet other community service responsibilities. Hospitals with high operating room (OR) utilizations can allocate OR time for elective surgery to surgeons based partly on their contribution margins per hour of OR time. This applies particularly when OR caseload is limited by nursing recruitment. From a hospital's annual accounting data for elective cases, we calculated the following for each surgeon's patients: variable costs for the entire hospitalization or outpatient visit, revenues, hours of OR time, hours of regular ward time, and hours of intensive care unit (ICU) time. The contribution margin per hour of OR time varied more than 1000% among surgeons. Linear programming showed that reallocating OR time among surgeons could increase the overall hospital contribution margin for elective surgery by 7.1%. This was not achieved simply by taking OR time from surgeons with the smallest contribution margins per OR hour and giving it to the surgeons with the largest contribution margins per OR hour because different surgeons used differing amounts of hospital ward and ICU time. We conclude that to achieve substantive improvement in a hospital's perioperative financial performance despite restrictions on available OR, hospital ward, or ICU time, contribution margin per OR hour should be considered (perhaps along with OR utilization) when OR time is allocated. IMPLICATIONS: For hospitals where elective surgery caseload is limited by nursing recruitment, to increase one surgeon's operating room time either another surgeon's time must be decreased, nurses need to be paid a premium for working longer hours, or higher-priced "traveling" nurses can be contracted. Linear programming was performed using Microsoft Excel to estimate the effect of each of these interventions on hospital contribution margin.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".