Dedicated operating room for emergency surgery improves access and efficiency
Bibliographic record
Abstract
BACKGROUND: Scheduling emergency cases among elective surgeries often results in prolonged waits for emergency surgery and delays or cancellation of elective cases. We evaluated the benefits of a dedicated operating room (OR) for emergency procedures available to all surgical services at a large children's hospital. METHODS: We compared a 6-month period (January 2009 to June 2009) preimplementation with a 6-month period (January 2010 to June 2010) postimplementation of a dedicated OR. We evaluated OR use, wait times, percentage of cases done within and outside of access targets, off-hours surgery, cancellations, overruns and length of stay. RESULTS: Preimplementation, 1069 of the 5500 surgeries performed were emergency cases. Postimplementation, 1084 of the 5358 surgeries performed were emergency cases. Overall use of the dedicated OR was 53% (standard deviation 25%) postimplementation. Excluding outliers, the average wait time for priority 3 emergency patients decreased from 11 hours 8 minutes to 10 hours 5 minutes (p = 0.004). An increased proportion of priority 3 patients, from 52% to 58%, received surgery within 12 hours (p = 0.020). There was a 9% decrease in the proportion of priority 3 cases completed during the evening and night (p < 0.001). The elective surgical schedule benefited from the dedicated OR, with a significant decrease in cancellations (1.5% v. 0.7%, p < 0.001) and an accumulated decrease of 5211 minutes in overrun minutes in elective rooms. The average hospital stay after emergency surgery decreased from 16.0 days to 14.7 days (p = 0.12) following implementation of the dedicated OR. CONCLUSION: A dedicated OR for emergency cases improved quality of care by decreasing cancellations and overruns in elective rooms and increasing the proportion of priority 3 patients who accessed care within the targeted time.
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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 | 0.001 | 0.008 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".