Improved patient safety with a simplified operating room to pediatric intensive care unit handover tool (PATHQS)
Bibliographic record
Abstract
Introduction: Patient handover is a crucial transition requiring a high level of coordination and communication. In the BC Children's Hospital (BCCH) pediatric intensive care unit (PICU), 10 adverse events stemming from issues that should have been addressed at the operating room (OR) to PICU handover were reported into the patient safety learning system (PSLS) within 1 year. We aimed to undertake a quality improvement project to increase adherence to a standardized OR to PICU handover process to 100% within a 6-month time frame. In doing so, the secondary aim was to reduce adverse events by 50% within the same 6-month period. Methods: The model for improvement and a Plan, Do, Study, Act method of quality improvement was used in this project. The adverse events were reviewed to identify root causes. The findings were reviewed by a multidisciplinary inter-departmental group comprised of members from surgery, anesthesia, and intensive care. Issues were batched into themes to address the most problematic parts of handover that were contributing to risk. Intervention: A bedside education campaign was initiated to familiarize the team with an existing handover standard. The project team then formulated a new simplified visual handover tool with the mnemonic "PATHQS" where each letter denoted a step addressing a theme that had been noted in the pre-intervention work as contributing to adverse events. Results: Adherence to standardized handover at 6 months improved from 69% to 92%. This improvement was sustained at 12 months and 3 years after the introduction of PATHQS. In addition, there were zero PSLS events relating to handover at 6 and 12 months, with only one filed by 36 months. Notably, staff self-reporting of safety concerns during handover reduced from 69% to 13% at 6 months and 0% at 3 years. The PATHQS tool created in this work also spread to six other units within the hospital as well as to one adult teaching hospital. Conclusion: A simplified handover tool built collaboratively between departments can improve the quality and adherence of OR to PICU handover and improve patient safety. Simplification makes it adaptable and applicable in many different healthcare 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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".