03110nas a2200325   4500000000100000008004100001100001700042700001400059700001200073700001600085700001400101700001500115700001200130700001700142700001700159700001400176700001200190700001400202700001700216700001600233700001500249700001800264700001500282245022400297250001500521300001000536490000600546520218100552020005102733       2015                            d1 aPanaretto K.1 aHarris M.1 aHunt J.1 aSullivan D.1 aLyford M.1 aJackson R.1 aZwar N.1 aColagiuri S.1 aUsherwood T.1 aHayman N.1 aCass A.1 aRedfern J1 aPeiris David1 aPatel Bindu1 aNeal Bruce1 aPatel Anushka1 aMacmahon S00aEffect of a computer-guided, quality improvement program for cardiovascular disease risk management in primary health care: the treatment of cardiovascular risk using electronic decision support cluster-randomized trial  a2015/01/15  a87-950 v83 a<p>
BACKGROUND: Despite effective treatments to reduce cardiovascular disease risk, their translation into practice is limited. METHODS AND RESULTS: Using 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 &gt;/= 35 years and others aged &gt;/= 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 &lt;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 (17.9% versus 2.7%; P&lt;0.001), lipid-lowering (19.2% versus 4.8%; P&lt;0.001), and blood pressure-lowering medications (23.3% versus 12.1%; P=0.02). CONCLUSIONS: In 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 URL: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=336630. Australian New Zealand Clinical Trials Registry No. 12611000478910.
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  a1941-7705 (Electronic)<br/>1941-7713 (Linking)