Improving patient flow in a regional anaesthesia block room
Bibliographic record
Abstract
University Hospital is a tertiary academic centre in London, Ontario, Canada. A designated space known as the block room (BR) supports a model of care to perform regional anaesthesia prior to entering the resource intense operating room (OR). Stress due to time pressure was reported by BR staff. It was presumed that upstream delays in patient admission, preparation, transportation and in the BR resulted in late OR starts. There was limited data for a patient's preoperative transit at our institution. A prospective quality improvement project was conceived to understand and address concerns surrounding patient flow. Using Plan-Do-Study-Act (PDSA) methodology, we collected baseline data of patients perioperative transit and performed three PDSA cycles for improvement. We established targets for OR entry time and patient arrival to the BR. We examined communication between the surgical preparation unit, BRandORs, involved stakeholders in decision making and continuously sourced feedback for improvement. Over three incremental rapid PDSA cycles and reaudit of our baseline, we found a statistically significant improvement in patients arriving to the BR 60 min prior to the scheduled OR time from a baseline of 31%-53% (p=0.04) and patient operations commencing on time improved from 52% to 65% (p=0.03). The availability of patients in the BR within 15 min of a decision to have them available reached 98% from a baseline of 69% (p<0.001). As a result of the quality improvement process, we were able to significantly improve the flow of the preoperative patient journey at our institution. With a better understanding of complex preoperative processes, we can strategically intervene and potentially improve efficiency, morale and 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".