Operating Room Interruptions: Insights and Mitigations from Four Hospitals Using OR Black Box ®
Bibliographic record
Abstract
Interoperative interruptions can strike at the worst possible moment, derailing focused workflow and increasing risk of harm. Past research largely relied on single-institution studies or aggregated data, limiting understanding of how interruptions vary by surgical task and setting. This thesis addresses that gap by analyzing 105 procedures across four hospitals using OR Black Box® to capture intraoperative interruptions. Interruptions were coded by task, source, reason, system factors. System factors were categorized as safety threats or resilience supports. In 48 laparoscopic general surgeries, surgical counts had highest interruption rates at Hospitals 1 and 2, while Hospital 3 showed lower rates due to resilience strategies (e.g., protected time). Analysis of 57 additional procedures revealed interruption patterns varied by procedure type (e.g., fewer interruptions in surgical counts for Hospital 2’s Minimally Invasive OBGYN versus General Laparoscopic). Findings point towards task-specific, system-level interventions to protect critical tasks, reduce workflow conflicts, and enhance surgical safety.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".