Improving operating room start times in a community teaching hospital
Bibliographic record
Abstract
Introduction: The operating room (OR) is an expensive entity to manage. Efficiency in hospital resource utilization is critical for hospital financial solvency. One measure of efficiency in the OR is percentage of on-time starts for cases at the beginning of each day. This study looks at a community teaching hospital where measures were taken to identify and address causes of tardiness in the OR.Methods: An interdisciplinary team of doctors, nurses, and other hospital staff came together to implement a three-phase agenda. In Phase I, staff identified causes of tardiness. In Phase II, potential solutions to address each specific task were drawn up. Phase III involved maintenance of efficiency measures created in Phase II and documentation of progress for future analysis.Results and Discussion: Over twelve months, the percentage of cases that started on time steadily increased from 14% to 68%. Additionally, of the cases that were late, the average number of minutes late decreased significantly. Of the identified causes of tardiness, surgeon arriving late was found to be the most prevalent. We analyzed the relationship between average minutes late each month and the cost and revenue per unit of service (UOS). Average minutes late per month and hospital revenue per UOS showed a strong inverse correlation of -0.83, while average minutes late per month and cost er UOS showed a moderate positive correlation of 0.62. We analyzed the relationship between average minutes late each month and the cost and revenue per UOS. Average minutes late per month and hospital revenue per UOS showed a strong inverse correlation of -0.83, while average minutes late per month and cost per UOS showed a moderate positive correlation of 0.62.Conclusions: Identifying causes of tardiness based on input from a multidisciplinary healthcare team and addressing each cause with a specific measure to combat it was effective in improving the percentage of on-time starts in the OR. We demonstrated that reducing delays in OR start times can both decrease cost and increase revenue. Documenting progress of efficiency measures is critical in distinguishing measures that work from those that do not. Furthermore, continued analysis of efficiency is required to maintain efficiency standards.
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How this classification was reachedexpand
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".