Evidence map›Paper›PMID 25515217›Full record

Trial reportImplementation science : IS2014

A multifaceted quality improvement intervention for CVD risk management in Australian primary healthcare: a protocol for a process evaluation.

Bindu Patel, Anushka Patel, Stephen Jan, Tim Usherwood, Mark Harris, Katie Panaretto, Nicholas Zwar, Julie Redfern, Jesse Jansen, Jenny Doust and 1 more

Open access · goldAbstract readMulticenter StudyRandomized Controlled Trial
In one paragraph

Trial report in Implementation science : IS, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed, 1 pooled it
5.1field-weighted citation impact, top 5% 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

14 citing papers in PubMed, 1 synthesis or guideline pooled it, 19 citations in OpenAlex.

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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

11 authors at 5 institutions in 1 country.

Bindu PatelThe George Institute for Global Health, University of Sydney, Sydney, NSW, 2006, Australia. bpatel@georgeinstitute.org.au.
Anushka PatelThe George Institute for Global Health, University of Sydney, Sydney, NSW, 2006, Australia. apatel@georgeinstitute.org.au.
Stephen JanThe George Institute for Global Health, University of Sydney, Sydney, NSW, 2006, Australia. sjan@georgeinstitute.org.au.
Tim UsherwoodUniversity of Sydney, Sydney, NSW, 2006, Australia. tim.usherwood@sydney.edu.au.
Mark HarrisUniversity of New South Wales, Sydney, NSW, 2052, Australia. m.f.harris@unsw.edu.au.
Katie PanarettoQueensland Aboriginal and Islander Health Council, 21 Buchanan St., West End, QLD, 4101, Australia. KatiePanaretto@qaihc.com.au.
Nicholas ZwarUniversity of New South Wales, Sydney, NSW, 2052, Australia. n.zwar@unsw.edu.au.
Julie RedfernThe George Institute for Global Health, University of Sydney, Sydney, NSW, 2006, Australia. jredfern@georgeinstitute.org.au.
Jesse JansenUniversity of Sydney, Sydney, NSW, 2006, Australia. jesse.jansen@sydney.edu.au.
Jenny DoustBond University, 14 University Dr, Robina, QLD, 4226, Australia. jdoust@bond.edu.au.
David PeirisThe George Institute for Global Health, University of Sydney, Sydney, NSW, 2006, Australia. dpeiris@georgeinstitute.org.au.
University of Sydney · AUThe George Institute for Global Health · AUUNSW Sydney · AUAboriginal Health Council of South Australia · AUBond University · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) is the leading cause of death and disability worldwide. Despite the widespread availability of evidence-based clinical guidelines and validated risk predication equations for prevention and management of CVD, their translation into routine practice is limited. We developed a multifaceted quality improvement intervention for CVD risk management which incorporates electronic decision support, patient risk communication tools, computerised audit and feedback tools, and monthly, peer-ranked performance feedback via a web portal. The intervention was implemented in a cluster randomised controlled trial in 60 primary healthcare services in Australia. Overall, there were improvements in risk factor recording and in prescribing of recommended treatments among under-treated individuals, but it is unclear how this intervention was used in practice and what factors promoted or hindered its use. This information is necessary to optimise intervention impact and maximally implement it in a post-trial context. In this study protocol, we outline our methods to conduct a theory-based, process evaluation of the intervention. Our aims are to understand how, why, and for whom the intervention produced the observed outcomes and to develop effective strategies for translation and dissemination. METHODS/

designWe will conduct four discrete but inter-related studies taking a mixed methods approach. Our quantitative studies will examine (1) the longer term effectiveness of the intervention post-trial, (2) patient and health service level correlates with trial outcomes, and (3) the health economic impact of implementing the intervention at scale. The qualitative studies will (1) identify healthcare provider perspectives on implementation barriers and enablers and (2) use video ethnography and patient semi-structured interviews to understand how cardiovascular risk is communicated in the doctor/patient interaction both with and without the use of intervention. We will also assess the costs of implementing the intervention in Australian primary healthcare settings which will inform scale-up considerations. DISCUSSION: This mixed methods evaluation will provide a detailed understanding of the process of implementing a quality improvement intervention and identify the factors that might influence scalability and sustainability. TRIALS REGISTRATION: 12611000478910.

Indexed as

Quality ImprovementAttitude of Health PersonnelAustraliaCardiovascular DiseasesCluster AnalysisDecision Support Systems, ClinicalHealth Knowledge, Attitudes, PracticeHumansPrimary Health CareProgram EvaluationRisk ManagementTranslational Research, Biomedical

Identifiers

PMID25515217
PMCPMC4279909
OpenAlexW2112524556

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.