Managing and avoiding delay in operating theatres: a qualitative, observational study
Bibliographic record
Abstract
RATIONALE, AIMS AND OBJECTIVES: A range of strategies have been proposed to identify and address operating theatre delays, including preoperative checklists, post-delay audits and staff education. These strategies provide a useful starting point in addressing delay, but their effectiveness can be increased through more detailed consideration of sources of surgical delay. METHOD: A qualitative, observational study was conducted at two Australian hospitals, one a metropolitan site and the other a regional hospital. Thirty surgeries were observed involving general, vascular and orthopaedic procedures which ranged in time from 20 minutes to almost 4 hours. Approximately 40 hours of observations were conducted in total. RESULTS: The research findings suggest that there are two key challenges involved in addressing operating theatre delays: unanticipated problems in the clinical condition of patients, and the capacity of surgeons to regulate their own time. These challenges create unavoidable delays due to the contingencies of surgical work and competing demands on surgeons' time. The results also found that surgical staff play a critical role in averting and anticipating delays. Differences in professional authority are significant in influencing how operating theatre time is managed. CONCLUSIONS: Strategies aimed at addressing operating theatre delays are unlikely to achieve their desired aims without a more detailed understanding of medical decision making and work practices, and the intra- as well as inter-professional hierarchies underpinning them. While the nature of surgical work poses some challenges for measures designed to address delays, it is also necessary to focus on surgical practice in devising workable solutions.
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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 | 0.021 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".