Development and implementation of a standardised emergency department intershift handover tool to improve physician communication
Bibliographic record
Abstract
BACKGROUND: Structured handover can reduce communication breakdowns and potential medical errors. In our emergency department (ED) we identified a safety risk due to variation in quality and content of overnight handovers between physicians. AIM: Our goal was to develop and implement a standardised ED-specific handover tool using quality improvement (QI) methodology. We aimed to increase the proportion of patients having adequate handover information conveyed at overnight shift change from a baseline of 50%-75% in 4 months. METHODS: We used published best practices, stakeholder input and local data to develop a tool customised for intershift ED handovers. Implementation methods included education, cognitive aids, policy change and plan-do-study-act cycles informed by end-user feedback. We monitored progress using direct observation convenience sampling. MEASURES: Our outcome measure was proportion of adequate patient handovers (defined as >50% of handover components communicated per patient) per overnight handover session. Tool utilisation characteristics were used for process measurement, and time metrics for balancing measures. We report changes using statistical process control charts and descriptive statistics. RESULTS: We observed 49 overnight handover sessions from 2017 to 2019, evaluating handovers of 850 patients. Our improvement target was met in 10 months (median=76.1%) and proportion of adequate handovers continued to improve to median=83.0% at the postimprovement audit. Written communication of handover information increased from a median of 19.2% to 68.7%. Handover time increased by median=31 s per patient. End-users subjectively reported improved communication quality and value for resident education. CONCLUSIONS: We achieved sustained improvements in the amount of information communicated during physician ED handovers using established QI methodologies. Engaging stakeholders in handover tool customisation for local context was an important success factor. We believe this approach can be easily adopted by any ED.
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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 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".