Among CVRM patients , the results tended towards a beneficial effect of CDSS , but only LDL-c target attainment in diabetes patients reached statistical significance .
Prompting , integration into the electronical health record , patient empowerment , and medication support were related to more effective CVRM .
Conclusion We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment .
Some features of CDSS seem more promising than others .
Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment
Background Cardiovascular risk management ( CVRM ) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease .
Computerized decision support systems ( CDSS ) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies .
The effect on cardiovascular risk factor management is yet to be confirmed .
Personalised physician learning intervention to improve hypertension and lipid control: randomised trial comparing two methods of physician profiling
Purpose To assess the impact of personalised physician learning ( PPL ) interventions using simulated learning cases on control of  hypertension and dyslipidaemia in primary care setting s. Methods A total of 132 primary care physicians , 4568 eligible patients with uncontrolled hypertension , and 15 392 eligible patients with uncontrolled dyslipidaemia were cluster-r and omised to one of three conditions : ( a ) no intervention , ( b ) PPL-electronic medical record ( EMR ) intervention in which 12 PPL cases were assigned to each physician based on observed patterns of care in the EMR in the previous year , or ( c ) PPL-ASSESS intervention in which 12 PPL cases were assigned to each physician based on their performance on four st and ardised assessment cases . General and generalised linear mixed models were used to account for clustering and to model differences in patient outcomes in the study arms . Results Among patients with uncontrolled hypertension at baseline , 49.1 % , 46.6 % and 47.3 % ( p=0.43 ) achieved blood pressure ( BP ) targets at follow-up . Among patients with uncontrolled dyslipidaemia at baseline , 37.5 % , 37.3 % and 38.1 % ( p=0.72 ) achieved low density lipoprotein cholesterol targets at follow-up in PPL-EMR , PPL-ASSESS and the control group , respectively . Although systolic ( BP ) ( p<0.001 ) and lipid ( p<0.001 ) values significantly improved during the study , the group-by-time interaction term showed no differential change in systolic BP values ( p=0.51 ) or lipid values ( p=0.61 ) among the three study arms . No difference in intervention effect was noted when comparing the PPL-EMR with the PPL-ASSESS intervention ( p=0.47 ) . Conclusions The two PPL interventions tested in this study did not lead to improved control of hypertension or dyslipidaemia in primary care clinics during a mean 14-month follow-up period . This null result may have been due in part to substantial overall improvement in BP and lipid control at the study sites during the study . Trial registration number NCT00903071
Among CVRM patients , the results tended towards a beneficial effect of CDSS , but only LDL-c target attainment in diabetes patients reached statistical significance .
Prompting , integration into the electronical health record , patient empowerment , and medication support were related to more effective CVRM .
Conclusion We did not find a clear clinical benefit from CDSS in cardiovascular risk factor levels and target attainment .
Some features of CDSS seem more promising than others .
Clinical relevance of CDSS in CVRM might additionally be sought in the improvement of shared decision making and patient empowerment
Background Cardiovascular risk management ( CVRM ) is notoriously difficult because of multi-morbidity and the different phenotypes and severities of cardiovascular disease .
Computerized decision support systems ( CDSS ) enable the clinician to integrate the latest scientific evidence and patient information into tailored strategies .
The effect on cardiovascular risk factor management is yet to be confirmed .