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 .
The Vermont Diabetes Information System: A Cluster Randomized Trial of a Population Based Decision Support System
BACKGROUND Optimal care for patients with diabetes is difficult to achieve in clinical practice . OBJECTIVE To evaluate the impact of a registry and decision support system on processes of care , and physiologic control . PARTICIPANTS R and omized trial with clustering at the practice level , involving 7,412 adults with diabetes in 64 primary care practice s in the Northeast . INTERVENTIONS Provider decision support ( reminders for overdue diabetes tests , alerts regarding abnormal results , and quarterly population reports with peer comparisons ) and patient decision support ( reminders and alerts ) . MEASUREMENTS AND MAIN RESULTS Process and physiologic outcomes were evaluated in all subjects . Functional status was evaluated in a r and om patient sample via question naire . We used multiple logistic regression to quantify the effect , adjusting for clustering and potential confounders . Intervention subjects were significantly more likely to receive guideline -appropriate testing for cholesterol ( OR = 1.39 ; [ 95%CI 1.07 , 1.80 ] P = 0.012 ) , creatinine ( OR = 1.40 ; [ 95%CI 1.06 , 1.84 ] P = 0.018 ) , and proteinuria ( OR = 1.74 ; [ 95%CI 1.13 , 1.69 ] P = 0.012 ) , but not A1C ( OR = 1.17 ; [ 95 % CI 0.80 , 1.72 ] P = 0.43 ) . Rates of control of A1C and LDL cholesterol were similar in the two groups . There were no differences in blood pressure , body mass index , or functional status . CONCLUSIONS A chronic disease registry and decision support system based on easily obtainable laboratory data was feasible and acceptable to patients and providers . This system improved the process of laboratory monitoring in primary care , but not physiologic control
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 .