Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure , total and low-density lipoprotein cholesterol , and hemoglobin A1C levels .
CONCLUSIONS CDSSs are effective in improving clinician practice s related to screening and other preventive care services , clinical tests , and treatments .
CONTEXT Clinical decision support systems ( CDSSs ) can help clinicians assess cardiovascular disease ( CVD ) risk and manage CVD risk factors by providing tailored assessment s and treatment recommendations based on individual patient data .
The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors , practice s for CVD-related preventive care services such as clinical tests and prescribed treatments , and management of CVD risk factors .
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
Results were inconsistent for changes in CVD risk factors such as systolic and diastolic blood pressure , total and low-density lipoprotein cholesterol , and hemoglobin A1C levels .
CONCLUSIONS CDSSs are effective in improving clinician practice s related to screening and other preventive care services , clinical tests , and treatments .
CONTEXT Clinical decision support systems ( CDSSs ) can help clinicians assess cardiovascular disease ( CVD ) risk and manage CVD risk factors by providing tailored assessment s and treatment recommendations based on individual patient data .
The goal of this systematic review was to examine the effectiveness of CDSSs in improving screening for CVD risk factors , practice s for CVD-related preventive care services such as clinical tests and prescribed treatments , and management of CVD risk factors .