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 .
Translating Cholesterol Guidelines Into Primary Care Practice: A Multimodal Cluster Randomized Trial
PURPOSE We wanted to determine whether an intervention based on patient activation and a physician decision support tool was more effective than usual care for improving adherence to National Cholesterol Education Program guidelines . METHODS A 1-year cluster r and omized controlled trial was performed using 30 primary care practice s ( 4,105 patients ) in southeastern New Engl and . The main outcome was the percentage of patients screened for hyperlipidemia and treated to their low-density lipoprotein ( LDL ) and non – high-density lipoprotein ( HDL ) cholesterol goals . RESULTS After 1 year of intervention , both r and omized practice groups improved screening ( 89 % screened ) , and 74 % of patients in both groups were at their LDL and non-HDL cholesterol goals ( P < .001 ) . Using intent-to-treat analysis , we found no statistically significant differences between practice groups in screening or percentage of patients who achieved LDL and non-HDL cholesterol goals . Post hoc analysis showed practice s who made high use of the patient activation kiosk were more likely to have patients screened ( odds ratio [ OR ] = 2.54 ; 95 % confidence interval [ CI ] , 1.97–3.27 ) compared with those who made infrequent or no use . Additionally , physicians who made high use of decision support tools were more likely to have their patients at their LDL cholesterol goals ( OR = 1.27 ; 95 % CI , 1.07–1.50 ) and non-HDL goals ( OR = 1.23 ; 95 % CI , 1.04–1.46 ) than low-use or no-use physicians . CONCLUSION This study showed null results with the intent-to-treat analysis regarding the benefits of a patient activation and a decision support tool in improving cholesterol management in primary care practice s. Post hoc analysis showed a potential benefit in practice s that used the e-health tools more frequently in screening and management of dyslipidemia . Further research on how to incorporate and increase adoption of user-friendly , patient-centered e-health tools to improve screening and management of chronic diseases and their risk factors is warranted
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 .