Communication failures in the operating room: an observational classification of recurrent types and effects
Bibliographic record
Abstract
BACKGROUND: Ineffective team communication is frequently at the root of medical error. The objective of this study was to describe the characteristics of communication failures in the operating room (OR) and to classify their effects. This study was part of a larger project to develop a team checklist to improve communication in the OR. METHODS: Trained observers recorded 90 hours of observation during 48 surgical procedures. Ninety four team members participated from anesthesia (16 staff, 6 fellows, 3 residents), surgery (14 staff, 8 fellows, 13 residents, 3 clerks), and nursing (31 staff). Field notes recording procedurally relevant communication events were analysed using a framework which considered the content, audience, purpose, and occasion of a communication exchange. A communication failure was defined as an event that was flawed in one or more of these dimensions. RESULTS: 421 communication events were noted, of which 129 were categorized as communication failures. Failure types included "occasion" (45.7% of instances) where timing was poor; "content" (35.7%) where information was missing or inaccurate, "purpose" (24.0%) where issues were not resolved, and "audience" (20.9%) where key individuals were excluded. 36.4% of failures resulted in visible effects on system processes including inefficiency, team tension, resource waste, workaround, delay, patient inconvenience and procedural error. CONCLUSION: Communication failures in the OR exhibited a common set of problems. They occurred in approximately 30% of team exchanges and a third of these resulted in effects which jeopardized patient safety by increasing cognitive load, interrupting routine, and increasing tension in the OR.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".