Analyzing and Discussing Human Factors Affecting Surgical Patient Safety Using Innovative Technology: Creating a Safer Operating Culture
Bibliographic record
Abstract
INTRODUCTION: Surgical errors often occur because of human factor-related issues. A medical data recorder (MDR) may be used to analyze human factors in the operating room. The aims of this study were to assess intraoperative safety threats and resilience support events by using an MDR and to identify frequently discussed safety and quality improvement issues during structured postoperative multidisciplinary debriefings using the MDR outcome report. METHODS: In a cross-sectional study, 35 standard laparoscopic procedures were performed and recorded using the MDR. Outcome data were analyzed using the automated Systems Engineering Initiative for Patient Safety model. The video-assisted MDR outcome report reflects on safety threat and resilience support events (categories: person, tasks, tools and technology, psychical and external environment, and organization). Surgeries were debriefed by the entire team using this report. Qualitative data analysis was used to evaluate the debriefings. RESULTS: A mean (SD) of 52.5 (15.0) relevant events were identified per surgery. Both resilience support and safety threat events were most often related to the interaction between persons (272 of 360 versus 279 of 400). During the debriefings, communication failures (also category person) were the main topic of discussion. CONCLUSIONS: Patient safety threats identified by the MDR and discussed by the operating room team were most frequently related to communication, teamwork, and situational awareness. To create an even safer operating culture, educational and quality improvement initiatives should aim at training the entire operating team, as it contributes to a shared mental model of relevant safety issues.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".