• David Peiris, Tim Usherwood, Kathryn Panaretto, Mark Harris, Jennifer Hunt, Julie Redfern, Nicholas Zwar, Stephen Colagiuri, Noel Hayman, Serigne Lo, Bindu Patel, Marilyn Lyford, Stephen MacMahon, Bruce Neal, David Sullivan, Alan Cass, Rod Jackson, and Anushka Patel.
• From The George Institute for Global Health, University of Sydney, Sydney, New South Wales, Australia (D.P., J.R., S.L., B.P., M.L., S.M., B.N., A.P.); Westmead Clinical School (T.U.), The Boden Institute (S.C.), and Sydney Medical School (D.S.) University of Sydney, New South Wales, Sydney, Australia; Queensland Aboriginal and Islander Health Council, Brisbane, Queensland, Australia (K.P.); Centre for Primary Health Care and Equity (M.H.) and School of Public Health and Community Medicine (N.Z.) University of New South Wales, Sydney, New South Wales, Australia; Aboriginal Health and Medical Research Council, Sydney, New South Wales, Australia (J.H.); Inala Indigenous Health Service, Queensland Health, Brisbane, Queensland, Australia (N.H.); Menzies School of Health Research, Darwin, Northern Territory, Australia (A.C.); and School of Population Health, University of Auckland, Auckland, New Zealand (R.J.). dpeiris@georgeinstitute.org.
• Circ Cardiovasc Qual. 2015 Jan 1; 8 (1): 87-95.

BackgroundDespite effective treatments to reduce cardiovascular disease risk, their translation into practice is limited.Methods And ResultsUsing a parallel arm cluster-randomized controlled trial in 60 Australian primary healthcare centers, we tested whether a multifaceted quality improvement intervention comprising computerized decision support, audit/feedback tools, and staff training improved (1) guideline-indicated risk factor measurements and (2) guideline-indicated medications for those at high cardiovascular disease risk. Centers had to use a compatible software system, and eligible patients were regular attendees (Aboriginal and Torres Strait Islander people aged ≥ 35 years and others aged ≥ 45 years). Patient-level analyses were conducted using generalized estimating equations to account for clustering. Median follow-up for 38,725 patients (mean age, 61.0 years; 42% men) was 17.5 months. Mean monthly staff support was <1 hour/site. For the coprimary outcomes, the intervention was associated with improved overall risk factor measurements (62.8% versus 53.4% risk ratio; 1.25; 95% confidence interval, 1.04-1.50; P=0.02), but there was no significant differences in recommended prescriptions for the high-risk cohort (n=10,308; 56.8% versus 51.2%; P=0.12). There were significant treatment escalations (new prescriptions or increased numbers of medicines) for antiplatelet (4.3% versus 2.7%; P=0.01), and BP lowering (18.2% versus 11.0%; P=0.02) but not lipid-lowering medications.ConclusionsIn Australian primary healthcare settings, a computer-guided quality improvement intervention, requiring minimal support, improved cardiovascular disease risk measurement but did not increase prescription rates in the high-risk group. Computerized quality improvement tools offer an important, albeit partial, solution to improving primary healthcare system capacity for cardiovascular disease risk management.Clinical Trial Registration Urlhttps://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336630. Australian New Zealand Clinical Trials Registry No. 12611000478910.© 2015 American Heart Association, Inc.

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