System Factors Affecting Patient Safety in the OR
Bibliographic record
Abstract
OBJECTIVE: The objective of this study is to determine the characteristics and frequency of intraoperative safety threats and resilience supports using a human factors measurement tool. BACKGROUND: Human factors analysis can provide insight into how system elements contribute to intraoperative adverse events. Empiric evidence on safety threats and resilience in surgical practice is lacking. METHODS: A cross-sectional study of 24 patients undergoing elective laparoscopic general surgery at a single center in the Netherlands from May to November, 2017 was conducted. Video, audio, and patient physiologic data from all included procedures were obtained through a multichannel synchronized recording device. Trained analysts reviewed the recordings and coded safety threats and resilience supports. The codes were categorized into 1 of 6 categories (person, task, tools and technology, physical environment, organization, and external environment). RESULTS: A median of 14 safety threats [interquartile range (IQR) 11-16] and 12 resilience supports (IQR 11-16) were identified per case. Most safety threat codes (median 9, IQR 7-12) and resilience support codes (median 10, IQR 7-12) were classified in the person category. The organization category contained a median of 2 (IQR 1-2) safety threat codes and 2 (IQR 2-3) resilience support codes per case. The tools and technology category contributed a small number of safety threats (median 1 per case, IQR 0-1), but rarely provided resilience support. CONCLUSIONS: Through a detailed human factors analysis of elective laparoscopic general surgery cases, this study provided a quantitative analysis of the existing safety threats and resilience supports in a modern endoscopic operating room.
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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 | 0.004 | 0.018 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".