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 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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".