Beyond the Surgical Safety Checklist
Bibliographic record
Abstract
BACKGROUND: The surgical safety checklist (SSC) has been credited with improving team situation awareness (SA) in the operating room. Although the SSC may support team SA at the outset of the operative case, intraoperative handoff provides an opportunity for either SA breakdown or, more preferably, SA reinforcement. High-functioning surgical teams demonstrate a high level of continued SA, whereas teams deficient in SA are more likely to be affected by surgical errors and adverse events. To date, no interprofessional intraoperative tools exist to support team SA beyond the SSC. METHODS: This study was divided into 2 phases. The first used qualitative methods to (1) characterize intraoperative handoff processes across surgery, nursing, anesthesia, and perfusion, and (2) identify cultural factors that shaped handoff practices. Data for phase one were collected over 38 observation days and 41 brief interviews. Phase 2, informed by phase 1, used a modified Delphi process to create a tool for use during intraoperative handoff. Data were analyzed iteratively. RESULTS: Handoff practices were not standardized and rarely involved the entire team. In addition we uncovered cultural factors-specifically assumptions held by participants-that hindered team communication during handoff. Assumptions included: (1) team members are interchangeable, (2) trained individuals are able to determine when it is appropriate to handoff without consulting the OR team. Despite claims of improved teamwork resulting from the SSC, many participants held a fragmented view of the OR team, resulting in communication challenges during handoff. Findings from both phases of our study informed the development of multidisciplinary intraoperative handoff tools to facilitate shared team situation awareness and a shared mental model. CONCLUSIONS: Intraoperative handoff occurs frequently, and offers the opportunity for either renewed or fractured team SA beyond the SSC.
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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 | 0.025 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".