Does the Surgical Safety Checklist need a co-pilot? Comparing adherence in gynecological surgery through electronic medical records and OR Black Box video observations
Bibliographic record
Abstract
BACKGROUND: Despite clear evidence that the Surgical Safety Checklist improves patient safety, the way its use is reported in the literature varies significantly. Consequently, we must understand the alignment between reported use of the checklist and its actual application to identify discrepancies that could affect safety reporting accuracy, and ultimately, patient safety outcomes. The study aims to examine Surgical Safety Checklist adherence in a gynecological operating room based on video data and to compare the resulting findings with reported use in patient electronic medical records. METHOD: An observational study was conducted on elective gynecological surgeries in a single operating room equipped with an OR Black Box from August to October 2021 to assess checklist compliance, quality, and engagement. The checklist's reported use in patient electronic medical records was reviewed. RESULTS: Forty-five surgeries were assessed. The video observed compliance score for Sign-in and Time-out was 100%, but 80% for Sign-out. Engagement scores, i.e., percentage of people paused, varied during the three checklist phases, with an overall mean score of 76% (range 45-94%). Quality scores, i.e., percentage of checklist items completed, differed between video observed (47% (95% CI 43-50)) and electronic medical records reported (89% (95% CI 84-94)) use. CONCLUSIONS: OR Black Box video provides a unique opportunity to assess the actual use of the Surgical Safety Checklist, revealing valuable insights into how it was used. Data showed that the checklist was not used as intended. A discrepancy was found between the reported completion in the electronic medical records and its actual use as observed in the video, with the former showing a much higher completion rate. This large discrepancy highlights the need for further initiatives to improve checklist use and reporting.
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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 | 0.016 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".