Improving surgical safety checklist completion using distributed responsibility of checklist item completion among operating room team members: A quality improvement project
Bibliographic record
Abstract
Background. Surgical safety checklists are a standard of care for safe operating room practice, but their use has not been associated with reductions in adverse perioperative outcomes in some settings. Non-adherence and partial checklist completion may contribute to this lack of effect. Objective. To examine whether a surgical safety checklist using distributed responsibility of checklist item completion, by allocation of questions and responses among operating room staff, increases surgical safety checklist compliance. Methods. With Quality and Risk Management approval, a multicomponent strategy consisting of novel surgical safety checklist focused on distributed responsibility of checklist item completion was evaluated in orthopaedic operating rooms at The Hospital for Sick Children, Toronto, from July to August 2016 using a before-and-after study design. The intervention consisted of a wall-mounted reusable checklist with questions and responses designated to specific operating room team members. Team training was provided beforehand, operating room team leaders were identified to promote the intervention, and revisions to the checklist content and process were implemented based on feedback on feasibility and clinical sensibility. Results. A total of 45 and 59 children were included in pre-intervention and intervention groups, respectively. Overall, 87% (1,354/1,560) of checklist items were observed. Checklist item completion was significantly increased in the post-intervention group (77% [615/802]) compared with the pre-intervention group (27% [150/522]) (P<0.001). Conclusions. These findings suggest that a multicomponent strategy of designating responsibility for item completion among operating room team members and using a memory aid can improve compliance with surgical safety checklist item completion.
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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 | 0.026 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".