Lost information during the handover of critically injured trauma patients: a mixed-methods study
Bibliographic record
Abstract
BACKGROUND: Clinical information may be lost during the transfer of critically injured trauma patients from the emergency department (ED) to the intensive care unit (ICU). The aim of this study was to investigate the causes and frequency of information discrepancies with handover and to explore solutions to improving information transfer. METHODS: A mixed-methods research approach was used at our level I trauma centre. Information discrepancies between the ED and the ICU were measured using chart audits. Descriptive, parametric and non-parametric statistics were applied, as appropriate. Six focus groups of 46 ED and ICU nurses and nine individual interviews of trauma team leaders were conducted to explore solutions to improve information transfer using thematic analysis. RESULTS: Chart audits demonstrated that injuries were missed in 24% of patients. Clinical information discrepancies occurred in 48% of patients. Patients with these discrepancies were more likely to have unknown medical histories (p<0.001) requiring information rescue (p<0.005). Close to one in three patients with information rescue had a change in clinical management (p<0.01). Participants identified challenges according to their disciplines, with some overlap. Physicians, in contrast to nurses, were perceived as less aware of interdisciplinary stress and their role regarding variability in handover. Standardising handover, increasing non-technical physician training and understanding unit cultures were proposed as solutions, with nurses as drivers of a culture of safety. CONCLUSION: Trauma patient information was lost during handover from the ED to the ICU for multiple reasons. An interprofessional approach was proposed to improve handover through cross-unit familiarisation and use of communication tools is proposed. Going beyond traditional geographical and temporal boundaries was deemed important for improving patient safety during the ED to ICU handover.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".