Implementing structured team debriefing using a Black Box in the operating room: surveying team satisfaction
Bibliographic record
Abstract
BACKGROUND: Surgical safety may be improved using a medical data recorder (MDR) for the purpose of postoperative team debriefing. It provides the team in the operating room (OR) with the opportunity to look back upon their joint performance objectively to discuss and learn from suboptimal situations or possible adverse events. The aim of this study was to investigate the satisfaction of the OR team using an MDR, the OR Black Box®, in the OR as a tool providing output for structured team debriefing. METHODS: In this longitudinal survey study, 35 gastro-intestinal laparoscopic operations were recorded using the OR Black Box® and the output was subsequently debriefed with the operating team. Prior to study, a privacy impact assessment was conducted to ensure alignment with applicable legal and regulatory requirements. A structured debrief model and an OR Back Box® performance report was developed. A standardized survey was used to measure participant's satisfaction with the team debriefing, the debrief model used and the performance report. Factor analysis was performed to assess the questionnaire's quality and identified contributing satisfaction factors. Multivariable analysis was performed to identify variables associated with participants' opinions. RESULTS: In total, 81 team members of various disciplines in the OR participated, comprising 35 laparoscopic procedures. Mean satisfaction with the OR Black Box® performance report and team debriefing was high for all 3 identified independent satisfaction factors. Of all participants, 98% recommend using the OR Black Box® and the outcome report in team debriefing. CONCLUSION: The use of an MDR in the OR for the purpose of team debriefing is considered to be both beneficial and important. Team debriefing using the OR Black Box® outcome report is highly recommended by 98% of team members participating.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".