Committed to Better Outcomes: Reducing Infection after Surgery Across the Ontario Surgical Quality Improvement Network
Bibliographic record
Abstract
BACKGROUND: In 2015, the Ontario Surgical Quality Improvement Network was established to create a community of practice for Ontario hospitals to improve surgical quality. A provincial campaign to decrease postsurgical infections was launched in 2017. STUDY DESIGN: Thirty hospitals implemented activities related to the campaign from April 2018 to March 2019. The community of practice was used to disseminate suggested change ideas in each area. Self-reported data from participating hospitals and collaborative-wide aggregate risk-adjusted data from the American College of Surgeons NSQIP were reviewed to determine the impact of the campaign on the rates of postoperative surgical site infections (SSIs), urinary tract infections (UTIs), and pneumonia. RESULTS: A total of 24, 8, and 2 hospitals selected SSIs, UTIs, and pneumonia, respectively, as their targets for improvement. Three hospitals selected both SSIs and UTIs, 1 hospital selected SSIs and pneumonia, and 1 hospital selected all 3 indicators as targets. Self-reported data demonstrated that the rates of SSIs and UTIs decreased significantly post campaign from 4.87% to 3.99% (p < 0.0001) and from 3.65% to 1.25% (p = 0.007), respectively. Pneumonia rates also decreased from 1.27% to 1.05%. Overall rates of SSIs, UTIs, and pneumonia across all Ontario Surgical Quality Improvement Network hospitals were reduced from 3.4%, 1.29%, and 0.88% to 3.37%, 1.14%, and 0.84%, respectively. CONCLUSIONS: The 1-year campaign resulted in a clinically significant reduction in the rates of SSIs and UTIs, as well as a trend for decrease in pneumonia incidence among participating hospitals. Using a flexible approach with priority setting and leveraging the community of practice for dissemination of change ideas is an effective way of sustaining quality improvement activities.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".