Implementation of an Innovative Technology Called the OR Black Box: A Feasibility Study
Bibliographic record
Abstract
Introduction . The operating room (OR) Black Box is an innovative technology that captures and compiles extensive real-time data from the OR, allowing identification and analysis of factors that influence intraoperative procedures and performances – ultimately improving patient safety. Implementation of this kind of technology is still an emerging research area and prone to face challenges. Methods . Observational study running from May 2017 to May 2021 conducted at Copenhagen University Hospital – Rigshospitalet, Denmark, involving 152 OR staff and 306 patients. Feasibility of the OR Black Box was assessed in accordance with Bowen’s framework with 8 focus areas. Results . The OR Black Box had a high level of acceptability among stakeholders with 100% participation from management, 93% from OR staff, and 98% from patients. The implementation process improved over time, and an average of 80% of the surgeries conducted were captured. The practical aspects such as numerous formal and informal meetings, ethical and legal approval, recruitment of patients were acceptable, albeit time-consuming. The OR Black Box was adopted without any changes in scheduled surgery program, but capturing hours were adjusted to match the surgery program and relocation of OR staff declining to provide consent was possible. Conclusions . Implementation of the OR Black Box was feasible yet challenging. Management, nearly all staff, and patients embraced the initiative; however, ongoing evaluation, information meetings, and commitment from stakeholders are required and crucial to sustain momentum, continue implementation and expansion. Ideas from this study can be useful in the implementation of similar initiatives.
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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 | 0.046 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".