Developing a business case for a regional anesthesia block room: up with efficiency, down with costs
Bibliographic record
Abstract
BACKGROUND: Regional anesthesia techniques offer many benefits for total joint arthroplasty (TJA) patients. However, they require personnel and equipment resources, as well as valuable operating room (OR) time. A block room offers a dedicated environment to perform regional anesthesia procedures while potentially offsetting costs. METHODS: The goal of this prospective quality improvement study was to develop a business case for implementation of a regional anesthesia block room and to demonstrate the cost-effectiveness of this program in decreasing OR time for TJA. All elective TJA patients presenting between January 2019 and March 2020 were included in our analysis. RESULTS: Our detailed business plan was approved by the hospital leadership. 561 patients in the preintervention group and 432 in the postintervention group were included for data analysis. Mean total OR time per surgical case decreased from 166 to 143 min for a difference of 23 min (95% CI 17 to 29). Similarly, anesthesia controlled OR time decreased from 46 min to 26 min for a difference of 20 min (95% CI 17 to 22). The block room resulted in an additional primary TJA case per daily OR list. The percentage of TJA patients receiving a peripheral nerve block increased from 63.1% to 87.0% (p<0.001). No safety events or block room associated OR delays were observed. CONCLUSION: Implementing a regional anesthesia block room required a comprehensive business plan for securing the necessary resources to support the program. The regional anesthesia block room is a cost-effective method to improve patient care and OR efficiency.
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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 | 0.028 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".