Sampling Error Can Significantly Affect Measured Hospital Financial Performance of Surgeons and Resulting Operating Room Time Allocations
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Bibliographic record
Abstract
UNLABELLED: Hospitals with limited operating room (OR) hours, those with intensive care unit or ward beds that are always full, or those that have no incremental revenue for many patients need to choose which surgeons get the resources. Although such decisions are based on internal financial reports, whether the reports are statistically valid is not known. Random error may affect surgeons' measured financial performance and, thus, what cases the anesthesiologists get to do and which patients get to receive care. We tested whether one fiscal year of surgeon-specific financial data is sufficient for accurate financial accounting. We obtained accounting data for all outpatient or same-day-admit surgery cases during one fiscal year at an academic medical center. Linear programming was used to find the mix of surgeons' OR time allocations that would maximize the contribution margin or minimize variable costs. Confidence intervals were calculated on these end points by using Fieller's theorem and Monte-Carlo simulation. The 95% confidence intervals for increases in contribution margins or reductions in variable costs were 4.3% to 10.8% and 6.0% to 8.9%, respectively. As many as 22% of surgeons would have had OR time reduced because of sampling error. We recommend that physicians ask for and OR managers get confidence intervals of end points of financial analyses when making decisions based on them. IMPLICATIONS: The common approach of using one fiscal year of perioperative accounting data can be insufficient to prevent random error from influencing important management decisions. When accounting data are used for hospital and operating room management decision making, confidence intervals should be calculated for the key financial variables (e.g., variable cost per hour of operating room time).
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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 | 0.002 | 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