Creating a high‐performance surgical safety checklist: A multimodal evaluation plan to reinvigorate the checklist
Bibliographic record
Abstract
RATIONALE, AIMS, AND OBJECTIVES: The WHO Surgical Safety Checklist is a communication tool designed to improve surgical safety processes and enhance teamwork. It has been widely adopted since its introduction over ten years ago. As surgical safety needs evolve, organizations should periodically review and update their checklists. A holistic evaluation of the checklist in the context of an organization is the first step to making informed updates. In this article, we describe a comprehensive but feasible strategy for checklist evaluation which we developed and implemented as part of a surgical safety initiative in a high-performing center. METHODS: A three-part evaluation plan was developed and carried out by a multidisciplinary team. The evaluation included assessment of 1. Quality of care through a review of surgical safety events; 2. Safety culture through a validated survey and informal feedback; and 3. Checklist performance through direct observations and a staff survey. To prepare for re-implementation the current institutional checklist was critically evaluated and a context assessment survey was administered to surgical staff. RESULTS: The evaluation revealed challenges in communication and teamwork, with surgical staff often perceived to be working in silos. The quality of care assessment indicated room for improvement in safety processes. Deficiencies in the safety culture measures of communication and feedback shed light on an overall lack of engagement with the checklist. Checklist performance demonstrated good adherence to the items on the checklist but limited engagement by the surgical team and minimal communication between subteams. These findings informed our revisions to the checklist and its implementation processes. CONCLUSIONS: We developed and implemented a comprehensive, scalable approach to checklist evaluation which directly informed improvements to the checklist that were tailored to the organization's current context. Organizations can apply this framework to breathe new life into their checklist and transform their safety culture.
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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 | 0.194 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".