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Record W4396609322 · doi:10.1136/bmjoq-2023-002647

Improving clinical reasoning and communication during handover: An intervention study of the BRIEF-C tool

2024· article· en· W4396609322 on OpenAlexafffund
Ghazwan Altabbaa, Tanya Beran, Marcia Clark, Elizabeth Oddone Paolucci

Bibliographic record

VenueBMJ Open Quality · 2024
Typearticle
Languageen
FieldMedicine
TopicHospital Admissions and Outcomes
Canadian institutionsUniversity of Calgary
FundersCumming School of Medicine, University of CalgaryAlberta Health Services
KeywordsHandoverIntervention (counseling)PsychologyComputer scienceMedicineNursingComputer network

Abstract

fetched live from OpenAlex

BACKGROUND: Existing handover communication tools often lack a clear theoretical foundation, have limited psychometric evidence, and overlook effective communication strategies for enhancing diagnostic reasoning. This oversight becomes critical as communication breakdowns during handovers have been implicated in poor patient care. To address these issues, we developed a structured communication tool: Background, Responsible diagnosis, Included differential diagnosis, Excluded differential diagnosis, Follow-up, and Communication (BRIEF-C). It is informed by cognitive bias theory, shows evidence of reliability and validity of its scores, and includes strategies for actively sending and receiving information in medical handovers. DESIGN: A pre-test post-test intervention study. SETTING: Inpatient internal medicine and orthopaedic surgery units at one tertiary care hospital. INTERVENTION: The BRIEF-C tool was presented to internal medicine and orthopaedic surgery faculty and residents who participated in an in-person educational session, followed by a 2-week period where they practised using it with feedback. MEASUREMENTS: Clinical handovers were audiorecorded over 1 week for the pre- and again for the post-periods, then transcribed for analysis. Two faculty raters from internal medicine and orthopaedic surgery scored the transcripts of handovers using the BRIEF-C framework. The two raters were blinded to the time periods. RESULTS: A principal component analysis identified two subscales on the BRIEF-C: diagnostic clinical reasoning and communication, with high interitem consistency (Cronbach's alpha of 0.82 and 0.99, respectively). One sample t-test indicated significant improvement in diagnostic clinical reasoning (pre-test: M=0.97, SD=0.50; post-test: M=1.31, SD=0.64; t(64)=4.26, p<0.05, medium to large Cohen's d=0.63) and communication (pre-test: M=0.02, SD=0.16; post-test: M=0.48, SD=0.83); t(64)=4.52, p<0.05, large Cohen's d=0.83). CONCLUSION: This study demonstrates evidence supporting the reliability and validity of scores on the BRIEF-C as good indicators of diagnostic clinical reasoning and communication shared during handovers.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.342

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.115
GPT teacher head0.511
Teacher spread0.396 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations4
Published2024
Admission routes2
Has abstractyes

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