P070: Improving handovers in the emergency department: implementation of a standardized team approach
Bibliographic record
Abstract
Introduction: Handovers in the ED are a high risk area for breakdown in team communication, discontinuity of patients’ clinical course, and potential medical errors. This is especially true for morning handovers at our center, when one single overnight MD working with limited resources hands over the entire ED to an oncoming day team of MDs and allied health professionals. We describe a quality improvement (QI) project to implement an inter-professional team approach during handovers. Methods: This prospective QI project took place at an academic tertiary care centre with >160,000 ED visits/yr. An expert working group identified key components of the ideal morning handover, and developed an intervention consisting of standardizing the “location”, “participants”, and “time” components of our handover processes. A research assistant directly observed all 8am handovers for 2 weeks pre- and 2 weeks post-intervention. Outcomes include participant attendance; # of beside RN issues proactively brought forward; frequency of new allied health consults and/or involvement triggered; # of physician interruptions; and time metrics. We report descriptive statistics. Results: During the study period a total of 308 individual patient handovers were observed [Pre:162, Post:146]. Average duration of total handover each morning decreased from 24.9min to 16.3min (p=0.051). Frequency of attendance at handovers increased for various allied health professionals, including care facilitators [Pre:35.7%; Post:91.7%, p=0.005], social workers [Pre:7.1%; Post:66.7%, p=0.003], geriatrics EM (GEM) RNs [Pre:64.3%; Post:83.3%, p=0.391], pharmacists [Pre:0.0%; Post:58.3%, p=0.001], and physiotherapists [Pre:0.0%; Post:58.3%, p=0.001]. Number of specific beside RN issues proactively brought forward increased [Pre:0; Post:4, p=0.049], while the number of physician interruptions during handover decreased [Pre:20; Post:0, p<0.0001]. Frequency of new allied health consults and/or involvement triggered as a result of handover participation increased from 6.8% to 13.7% (p=0.057). Conclusion: Implementation of a standardized team approach to morning handovers in the ED led to significant improvements in inter-professional contributions to patient care plans and overall efficiency. Future planned phases will build on this QI initiative by standardizing specific content of ED handovers.
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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 | 0.009 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".