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Record W2187750392 · doi:10.1136/bmjqs-2014-003903

Lost information during the handover of critically injured trauma patients: a mixed-methods study

2015· article· en· W2187750392 on OpenAlexaff
Tanya L. Zakrison, Brittany Rosenbloom, Amanda McFarlan, Aleksandra Jovičić, Sophie Soklaridis, Casey J. Allen, Carl I. Schulman, Nicholas Namias, Sandro Rizoli

Bibliographic record

VenueBMJ Quality & Safety · 2015
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsCentre for Addiction and Mental Health
Fundersnot available
KeywordsMedicineCritically illMedical emergencyHandoverIntensive care medicineEmergency medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.124

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.035
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0030.002
Scholarly communication0.0040.003
Open science0.0020.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.036
GPT teacher head0.423
Teacher spread0.387 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations58
Published2015
Admission routes1
Has abstractyes

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