P063 Enhancing patient safety in regional anesthesia: lessons learned from wrong-side block events
Bibliographic record
Abstract
Please confirm that an ethics committee approval has been applied for or granted: Not relevant (see information at the bottom of this page) Background and Aims Wrong-side blocks (WSBs) are a rare but serious complication in regional anesthesia. Anesthesia providers at our institution performed an average of 5,000 regional blocks annually across four block rooms. Acknowledging the grave repercussions of inadvertent WSBs, this quality improvement project focuses on investigating contributing factors and proposes preventive strategies, aiming to enhance patient safety. Methods An anonymous survey assessed WSB occurrences and near-miss events within our institution over the past three years. We analyzed the data to identify potential root causes. Results Despite safety protocols, four WSBs occurred, all deemed avoidable. Time pressure (32%), increased time between checklist and block (20%), change of assisting nurse (20%), checklist by another person (16%), and change of block performer (12%) were identified as contributing factors. Notably, one WSB resulted from unfamiliar prone positioning practices affecting landmark and ultrasound usage. Conclusions Factors such as time constraints, communication breakdowns, and procedural variations potentially contribute to the risk of WSB incidents. To mitigate these, we advocate for the implementation of a standardized safety checklist, documented electronically. It is imperative to allocate sufficient time for each procedural block to alleviate time constraints. Additionally, improving communication through handoff protocols and reducing the duration between checklist completion and block execution is paramount. Furthermore, comprehensive WSB prevention training should be imparted to all block room members. These strategies are designed to minimize the occurrence of WSB incidents and optimize patient 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 | 0.014 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.014 | 0.004 |
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".