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 .
Impact of Electronic Health Record Clinical Decision Support on Diabetes Care: A Randomized Trial
PURPOSE We wanted to assess the impact of an electronic health record – based diabetes clinical decision support system on control of hemoglobin A1c ( glycated hemoglobin ) , blood pressure , and low-density lipoprotein ( LDL ) cholesterol levels in adults with diabetes . METHODS We conducted a clinic-r and omized trial conducted from October 2006 to May 2007 in Minnesota . Included were 11 clinics with 41 consenting primary care physicians and the physicians ’ 2,556 patients with diabetes . Patients were r and omized either to receive or not to receive an electronic health record (EHR)–based clinical decision support system design ed to improve care for those patients whose hemoglobin A1c , blood pressure , or LDL cholesterol levels were higher than goal at any office visit . Analysis used general and generalized linear mixed models with repeated time measurements to accommo date the nested data structure . RESULTS The intervention group physicians used the EHR-based decision support system at 62.6 % of all office visits made by adults with diabetes . The intervention group diabetes patients had significantly better hemoglobin A1c ( intervention effect −0.26 % ; 95 % confidence interval , −0.06 % to −0.47 % ; P=.01 ) , and better maintenance of systolic blood pressure control ( 80.2 % vs 75.1 % , P=.03 ) and borderline better maintenance of diastolic blood pressure control ( 85.6 % vs 81.7 % , P = .07 ) , but not improved low-density lipoprotein cholesterol levels ( P = .62 ) than patients of physicians r and omized to the control arm of the study . Among intervention group physicians , 94 % were satisfied or very satisfied with the intervention , and moderate use of the support system persisted for more than 1 year after feedback and incentives to encourage its use were discontinued . CONCLUSIONS EHR-based diabetes clinical decision support  significantly improved glucose control and some aspects of blood pressure control in adults with type 2 diabetes
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 .