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 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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".