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 .
Bridging the quality gap in diabetic hyperlipidemia: a practice-based intervention.
PURPOSE Dyslipidemia treatment dramatically decreases coronary heart disease risk in diabetes , yet only a minority of these patients are screened or achieve optimal low-density lipoprotein ( LDL ) cholesterol levels . Our aim was to increase the percentage of diabetic patients in whom lipid management was achieved through electronic and direct educational detailing . METHODS The study cohort comprised 884 diabetic patients at 12 primary care practice s. Practice sites were r and omized to one of three intervention groups : electronic educational detailing , direct ( face-to-face ) educational detailing , or control . Direct and electronic detailing were performed over a 12-month period . All sites were notified of our goal to enhance lipid testing among diabetic patients . Chart abstract ion was performed 15 months after the start of the intervention . For the entire population ( n=884 ) , the proportion of patients with lipid testing was calculated , and changes from pre- to postintervention were compared across groups . We compared pre- and postintervention LDL-cholesterol changes between groups using least square means to account for site variation . RESULTS Favorable provider actions increased significantly with the intervention ( + 22 % compared with + 6 % in controls , P=.01 ) . By logistic regression , electronic detailing increased the likelihood of lipid testing ( odds ratio 3.0 , confidence interval 1.6 - 5.7 ) , as did direct detailing ( odds ratio 1.8 , confidence interval 0.9 - 3.7 ) in patients with no preintervention LDL test ( n=432 ) . Lipid testing tended to increase to a greater extent at intervention sites ( + 23 % for the combination of electronic and direct detailing vs + 11 % for controls , P=.06 ) . CONCLUSIONS Brief educational detailing either through direct or electronic communication favorably impacts provider behavior regarding dyslipidemia care for diabetic patients
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 .