Enhancing Transfer of Accountability in Burn Intensive Care Unit Nursing: A Quality Improvement Initiative
Bibliographic record
Abstract
In the burn intensive care unit (ICU), effective nurse handovers are critical to patient safety. Communication gaps during the transfer of accountability (TOA) contribute to preventable safety incidents. We designed a quality improvement (QI) initiative to standardize TOA and improve safety culture. A baseline safety culture survey of 31 burn ICU nurses and a 3-month review of incident reports (mean: 18/month) identified handover-related communication failures, including omitted treatments, delayed wound care, and missed monitoring responsibilities. We co-developed a structured, burn-specific TOA tool with frontline nurses and introduced it through targeted education. The intervention was implemented over eight weekly Plan-Do-Study-Act (PDSA) cycles. Outcomes included incident rates, nurse-reported safety culture, and process adherence. Postintervention, safety incidents decreased by 50% (from 18 to 9/month), and TOA-related safety culture scores improved by 20%, achieving both SMART objectives. Tool adherence exceeded 90% by the final cycle. Nurses reported improved clarity, reduced cognitive load, and enhanced interprofessional communication. No adverse workflow impacts were observed. A co-designed TOA tool, integrated with education and iterative PDSA refinement, significantly improved handover safety and reduced incidents in the burn ICU. This initiative provides a practical, scalable model for enhancing communication and safety culture in high-risk clinical settings.
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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 | 0.024 | 0.050 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".