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期刊名:Bmj quality & safety

缩写:BMJ QUAL SAF

ISSN:2044-5415

e-ISSN:2044-5423

IF/分区:6.5/Q1

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共收录本刊相关文章索引2220
Clinical Trial Case Reports Meta-Analysis RCT Review Systematic Review
Classical Article Case Reports Clinical Study Clinical Trial Clinical Trial Protocol Comment Comparative Study Editorial Guideline Letter Meta-Analysis Multicenter Study Observational Study Randomized Controlled Trial Review Systematic Review
Anna Melvin,Alison Pearson,Daniele Carrieri et al. Anna Melvin et al.
Introduction: The vital role of medical workforce well-being for improving patient experience and population health while assuring safety and reducing costs is recognised internationally. Yet the persistence of poor well-...
Karl T Chamberlin,Christopher DiTullio,Jennifer Rossman et al. Karl T Chamberlin et al.
Background: Evaluation of neck trauma is a common reason for emergency department (ED) visits. There are several validated clinical decision rules, such as the National Emergency X-Radiography Utilization Study (NEXUS) Ce...
Helen Crocker,David A Cromwell,Shivali Modha et al. Helen Crocker et al.
Objectives: The aim of this article is to provide an estimate of the proportion of the general public reporting healthcare-related harm in Great Britain, its location, impact, responses post-harm and desired reactions fro...
Kea Turner,Mona Al Taweel,Carrie Petrucci et al. Kea Turner et al.
Objectives: Many hospitals use fall prevention alarms, despite the limited evidence of effectiveness. The objectives of this study were (1) to identify, conceptualise and select strategies to deimplement fall prevention a...
Insook Cho,Joon-Myoung Kwon,Whasuk Choe et al. Insook Cho et al.
Background: Inpatient falls are adverse events that often result in injury due to complex interactions between the hospital environment and patient risk factors and remain a significant problem in clinical settings. ...
Rudolf Schnetler,Anton van der Vegt,Vikrant R Kalke et al. Rudolf Schnetler et al.
Objective: To identify bias in using a single machine learning (ML) sepsis prediction model across multiple hospitals and care locations; evaluate the impact of six different bias mitigation strategies and propose a gener...