Evidence map›Paper›PMID 26644128›Full record

ArticleBMJ open2015

Deepening our Understanding of Quality in Australia (DUQuA): a study protocol for a nationwide, multilevel analysis of relationships between hospital quality management systems and patient factors.

Natalie Taylor, Robyn Clay-Williams, Emily Hogden, Victoria Pye, Zhicheng Li, Oliver Groene, Rosa Suñol, Jeffrey Braithwaite

Open access · goldAbstract read
In one paragraph

Article in BMJ open, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
3.8field-weighted citation impact, top 7% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 20 citations in OpenAlex.

  1. Article
  2. Descriptive Epidemiology of Safety Events at an Academic Medical Center.International journal of environmental research and public health · 2020
    Article
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 3 institutions in 3 countries.

Natalie TaylorAustralian Institute of Health Innovation, Macquarie University, New South Wales, Australia.
Robyn Clay-WilliamsAustralian Institute of Health Innovation, Macquarie University, New South Wales, Australia.
Emily HogdenAustralian Institute of Health Innovation, Macquarie University, New South Wales, Australia.
Victoria PyeAustralian Institute of Health Innovation, Macquarie University, New South Wales, Australia.
Zhicheng LiAustralian Institute of Health Innovation, Macquarie University, New South Wales, Australia.
Oliver GroeneFaculty of Public Health and Policy, London School of Hygiene & Tropical Medicine, London, UK.
Rosa SuñolAvedis Donabedian Research Institute (FAD), Universitat Autonoma de Barcelona, Barcelona, Spain Red de investigación en servicios de salud en enfermedades crónicas REDISSEC, Barcelona, Spain.
Jeffrey BraithwaiteAustralian Institute of Health Innovation, Macquarie University, New South Wales, Australia.
Macquarie University · AUFaculty of Public Health · GBUniversitat Autònoma de Barcelona · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDespite the growing body of research on quality and safety in healthcare, there is little evidence of the association between the way hospitals are organised for quality and patient factors, limiting our understanding of how to effect large-scale change. The 'Deepening our Understanding of Quality in Australia' (DUQuA) study aims to measure and examine relationships between (1) organisation and department-level quality management systems (QMS), clinician leadership and culture, and (2) clinical treatment processes, clinical outcomes and patient-reported perceptions of care within Australian hospitals. METHODS AND ANALYSIS: The DUQuA project is a national, multilevel, cross-sectional study with data collection at organisation (hospital), department, professional and patient levels. Sample size calculations indicate a minimum of 43 hospitals are required to adequately power the study. To allow for rejection and attrition, 70 hospitals across all Australian jurisdictions that meet the inclusion criteria will be invited to participate. Participants will consist of hospital quality management professionals; clinicians; and patients with stroke, acute myocardial infarction and hip fracture. Organisation and department-level QMS, clinician leadership and culture, patient perceptions of safety, clinical treatment processes, and patient outcomes will be assessed using validated, evidence-based or consensus-based measurement tools. Data analysis will consist of simple correlations, linear and logistic regression and multilevel modelling. Multilevel modelling methods will enable identification of the amount of variation in outcomes attributed to the hospital and department levels, and the factors contributing to this variation. ETHICS AND DISSEMINATION: Ethical approval has been obtained. Results will be disseminated to individual hospitals in de-identified national and international benchmarking reports with data-driven recommendations. This ground-breaking national study has the potential to influence decision-making on the implementation of quality and safety systems and processes in Australian and international hospitals.

Indexed as

AustraliaClinical ProtocolsCross-Sectional StudiesHospitalsHumansLinear ModelsLogistic ModelsMultilevel AnalysisOrganizational CultureOutcome and Process Assessment, Health CarePatient Outcome AssessmentQuality Assurance, Health Carehospital performancehospital quality management systemspatient level factorspatient safetyquality improvement

Identifiers

PMID26644128
PMCPMC4679999
OpenAlexW2195308221

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.