Understanding safety threats and resilience supports in the operating room: a mixed-methods protocol study using surgical video analysis and clinician interviews
Bibliographic record
Abstract
INTRODUCTION: Preventable intraoperative adverse events (iAEs) are common despite widespread implementation of surgical quality improvement initiatives. These events often result from the interaction of multiple system-based factors (safety threats, STs) that coalesce to compromise safety. Existing research does not fully capture how STs vary across institutions, and how surgical teams either recover from or anticipate challenges (resilience supports, RSs). Consequently, efforts to design and align interventions are hindered by an incomplete understanding of the system-level contributors to patient safety risks. This study uses a human factors approach to gain a comprehensive understanding of STs and RSs across four hospitals by analysing operating room (OR) video recordings and conducting interviews with clinical teams to contextualise STs and RSs. METHODS AND ANALYSIS: This mixed-method study will analyse 120 surgical video recordings from four hospitals, using a comprehensive multimodal data capture platform, called OR Black Box (ORBB, Surgical Safety Technologies New York City, USA). All ORBB videos will be coded for case information, surgical phase, iAE type and severity. Human factor researchers will then retrospectively identify and code STs and RSs, applying a combined deductive (Systems Engineering Initiative for Patient Safety components: person, tasks, tools/technology, environment, organisation) and inductive approach. Detailed qualitative observations of STs and RSs will be transcribed, with the roles of the involved individuals noted. Quantitative and qualitative cross-institutional comparisons will highlight potential effective interventions (eg, radiofrequency sponge detection wands used during surgical counts) at specific sites, offering insights that could inform potential improvements at other institutions. Additionally, interviews with clinicians at each site will provide contextual insights into the prevalent STs and RSs. ETHICS AND DISSEMINATION: Ethics approval was obtained from the research ethics boards of: North York General Hospital (REB #2024-0174-993), a large Canadian community academic hospital; Sunnybrook Health Sciences Centre (REB #5779; REB #6688) and Unity Health Toronto (REB #16243), large Canadian academic hospitals and the Panel on Human Subjects Medical Research of Stanford University (IRB #6208), for its large American academic hospital. Results will be published in peer-reviewed journals, presented at conferences and to stakeholders.
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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 | 0.078 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".