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 .
Electronic Alerts Versus On-Demand Decision Support to Improve Dyslipidemia Treatment: A Cluster Randomized Controlled Trial
Background — Indirect evidence shows that alerting users with clinical decision support systems seems to change behavior more than requiring users to actively initiate the system . However , r and omized trials comparing these methods in a clinical setting are lacking . We studied the effect of both alerting and on-dem and decision support with respect to screening and treatment of dyslipidemia based on the guidelines of the Dutch College of General Practitioners . Methods and Results — In a clustered r and omized trial design , 38 Dutch general practice s ( 77 physicians ) and 87 886 of their patients ( 39 433 men 18 to 70 years of age and 48 453 women 18 to 75 years of age ) who used the ELIAS electronic health record participated . Each practice was assigned to receive alerts , on-dem and support , or no intervention . We measured the percentage of patients screened and treated after 12 months of follow-up . In the alerting group , 65 % of the patients requiring screening were screened ( relative risk versus control=1.76 ; 95 % confidence interval , 1.41 to 2.20 ) compared with 35 % of patients in the on-dem and group ( relative risk versus control=1.28 ; 95 % confidence interval , 0.98 to 1.68 ) and 25 % of patients in the control group . In the alerting group , 66 % of patients requiring treatment were treated ( relative risk versus control=1.40 ; 95 % confidence interval , 1.15 to 1.70 ) compared with 40 % of patients ( relative risk versus control=1.19 ; 95 % confidence interval , 0.94 to 1.50 ) in the on-dem and group and 36 % of patients in the control group . Conclusion — The alerting version of the clinical decision support systems significantly improved screening and treatment performance for dyslipidemia by general practitioners
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 .