Amplifiers and Dampeners of Patient Safety Risk in the Operating Room: Interim Analysis of Surgical Video Recorded with the Operating Room Black Box
Bibliographic record
Abstract
Preventable intraoperative adverse events (iAEs) may emerge from interactions between multiple work system factors (WSFs) (e.g., technology design, organizational policy, physical environment); these interactions may amplify or dampen patient safety risk. We conducted an exploratory observational study using audiovisual data captured by the Operating Room Black Box to characterize the relationships between associated WSFs. Human factors specialists reviewed video recordings of surgical procedures before transcribing events of interest and classifying them into the relevant WSF categories as defined by the Systems Engineering Initiative for Patient Safety model. Each WSF code was categorized as either a safety threat (ST) or resilience support (RS), and their interactions with associated WSFs were characterized. We transcribed 706 events over 73.5 hours of surgery, and 32 iAEs were identified. We coded 382 STs and 312 RSs, and 249 co-occurring WSF pairings. Co-occurring team (e.g., clear communication, feedback, and leadership) RSs were found to be the most prevalent mechanism to dampen all categories of ST. Co-occurring task (e.g., challenging anatomy) and environment (e.g., disruptive working environments, suboptimal ergonomic monitor setups) STs were the most common risk amplifiers contributing to the occurrence of iAEs. By assessing WSFs in the context of other WSFs, future research may develop interventions that more precisely target risk reduction in the 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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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".