Medical handovers across shifts within a five-day-working model: results from an electronic handover system in an acute NHS trust
Bibliographic record
Abstract
Electronic handover tools have been advocated as a potential strategy to improve the quality of handover, especially during on-call periods at night and weekends. We aimed to quantify, categorise and explore the temporal relationship of handover tasks stored on an electronic handover system (eHandover) in an acute UK hospital trust in which the day-time primary team worked only weekdays, with only the day-time and night-time on-call teams being available at weekends. Second, we evaluated whether tasks that remained in the eHandover system throughout several shifts were likely to be completed. We defined the shift gap as the number of clinical shifts that passed between the creation of the handover task and its completion. 11,071 electronic handover parcels created on eHandover between March 2010 and January 2011 were analysed. More handovers were requested for completion on weekends (70 parcels a day) than on routine weekdays (22 parcels a day; p<0.001). The receiving teams reported that 89.4% (9,900) of the handover parcels were completed. Greater amounts of handover work was requested over weekends, when tasks were often transferred across many clinical shifts. Despite this, task-completion rates on eHandover remained consistently high. The use of a well-designed electronic handover system as part of a systematic intervention, in combination with organised verbal handover meetings, can help to reduce the risk of communication failure across shifts.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".