MétaCan
Menu
Back to cohort
Record W4414649421 · doi:10.1136/bmjqs-2025-019170

Impact of medical safety huddles on patient safety: a stepped-wedge cluster randomised study

2025· article· en· W4414649421 on OpenAlexaff
Meiqi Guo, Mark Bayley, Xiang Y. Ye, Richard Dunbar‐Yaffe, Chris Fortin, Katharyn Go, Alyssa Macedo, John Matelski, Amanda L. Mayo, Jordan Pelc, Lawrence R. Robinson, Leahora Rotteau, Jesse Wolfstadt, Peter Cram, Lauren Linett, Christine Soong

Bibliographic record

VenueBMJ Quality & Safety · 2025
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsHealth Sciences CentreTD Bank GroupPrincess Margaret Cancer CentreSunnybrook Health Science CentreSinai Health SystemUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsCluster (spacecraft)Patient safetyMedical deviceMEDLINEQuality managementHealth services research

Abstract

fetched live from OpenAlex

BACKGROUND: Medical safety huddles are short, structured meetings for physicians to proactively discuss and respond to profession-specific patient safety concerns, with the goal of decreasing future adverse events. Prior observational studies found associations with improved patient safety outcomes, but no randomised controlled studies have been conducted. OBJECTIVE: The primary objective was to determine the impact of medical safety huddles on adverse events. Secondary objectives included the fidelity of huddle implementation and the impact on patient safety culture among physicians. DESIGN: Stepped-wedge cluster randomised trial with four sequences, and each hospital site was a cluster. SETTING: Inpatient oncology, surgery and rehabilitation programmes in four academic hospitals. PARTICIPANTS: Physicians in participating programmes. INTERVENTION: Medical safety huddles were adapted for local context and implemented sequentially based on a computer-generated random sequence every 2 months after a 4-month control period. All sites remained in the intervention phase for at least 9 months. MAIN OUTCOME AND MEASURES: The primary outcome was the rate of adverse events, as determined through blinded chart audits of 912 randomly selected patients. The fidelity of implementation was assessed through the huddle attendance rate, number of safety issues raised in the huddles and number of actions taken in response. Patient safety culture was assessed using the Agency for Healthcare Research and Quality Hospital Survey on Patient Safety. RESULTS: The adjusted rate of adverse events (per 1000 patient days) in the postintervention phase was 12% lower compared with preintervention (RR: 0.88; 95% CI: 0.80 to 0.98; p=0.016). The odds of having adverse events posthuddle implementation were 17% lower in the postintervention period compared with preintervention (OR intervention vs control: 0.83; 95% CI: 0.80 to 0.87; p<0.001). The mean huddle attendance rate at each site ranged from 30% to 85%, and the mean number of issues raised per huddle and the mean number of actions taken per huddle ranged from 1.6 to 3.1. The mean (SD) overall patient safety rating increased from 2.3 (0.53) to 2.8 (0.88), p=0.010. The mean per cent (SD) positive score for the composite measures of 'Organisational learning' increased significantly from 35% (26%) to 54% (23%), p=0.00, 'Response to error' 37% (24%) to 52% (22%), p=0.025 and 'Communication about error' 36% (28%) to 64% (42%), p=0.016 after implementation. CONCLUSIONS AND RELEVANCE: Medical safety huddles decreased adverse events and may improve patient safety culture through engaging physicians. TRIAL REGISTRATION NUMBER: NCT05365516.

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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.

Opus teacher head0.102
GPT teacher head0.535
Teacher spread0.432 · 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 designRandomized trial
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

Citations1
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueBMJ Quality & SafetySame topicPatient Safety and Medication ErrorsFrench-language works237,207