Accelerating surgical quality improvement in Ontario through a regional collaborative: a quality-improvement study
Bibliographic record
Abstract
BACKGROUND: The American College of Surgeons National Surgical Quality Improvement Program (NSQIP) collaborative in Ontario, the Ontario Surgical Quality Improvement Network (ON-SQIN), was launched in January 2015. We describe its approaches to support surgical quality improvement and examine its early impact on member hospitals. METHODS: All Ontario hospitals that participated in the ON-SQIN and NSQIP were included in this quality-improvement study. The primary intervention was the introduction of the ON-SQIN, and the secondary interventions included a community of practice and access to quality-improvement resources and tools. Outcome measures included the level of quality-improvement capacity, collaborative-wide aggregate data on postoperative complications, and self-reported rates of surgical site and urinary tract infections. RESULTS: Eighteen hospitals that enrolled in the ON-SQIN in 2015 reported an increase in their capacity for quality improvement after 18 months. Analysis of the collaborative-wide aggregate data in a 6-month period (14 748 surgical cases) revealed a substantial reduction of acute renal failure (relative risk 0.48, 95% confidence interval 0.25-0.95) and urinary tract infection (relative risk 0.77, 95% confidence interval 0.61-0.97). Most hospitals that targeted prevention of surgical site infection and urinary tract infection reported reduction of these occurrences during a 1-year period. INTERPRETATION: The ON-SQIN supported the uptake of the NSQIP in Ontario hospitals and promoted targeted surgical quality-improvement initiatives, resulting in increased quality-improvement capacity and development of the community of practice. Furthermore, our early experience suggests that improvements in surgical care are being realized.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.008 | 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".