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 .
Protocol for the 'e-Nudge trial': a randomised controlled trial of electronic feedback to reduce the cardiovascular risk of individuals in general practice [ISRCTN64828380]
Background Cardiovascular disease ( including coronary heart disease and stroke ) is a major cause of death and disability in the United Kingdom , and is to a large extent preventable , by lifestyle modification and drug therapy . The recent st and ardisation of electronic codes for cardiovascular risk variables through the United Kingdom 's new General Practice contract provides an opportunity for the application of risk algorithms to identify high risk individuals . This r and omised controlled trial will test the benefits of an automated system of alert messages and practice search es to identify those at highest risk of cardiovascular disease in primary care data bases . Design Patients over 50 years old in practice data bases will be r and omised to the intervention group that will receive the alert messages and search es , and a control group who will continue to receive usual care . In addition to those at high estimated risk , potentially high risk patients will be identified who have insufficient data to allow a risk estimate to be made . Further groups identified will be those with possible undiagnosed diabetes , based either on elevated past recorded blood glucose measurements , or an absence of recent blood glucose measurement in those with established cardiovascular disease . Outcome measures The intervention will be applied for two years , and outcome data will be collected for a further year . The primary outcome measure will be the annual rate of cardiovascular events in the intervention and control arms of the study . Secondary measures include the proportion of patients at high estimated cardiovascular risk , the proportion of patients with missing data for a risk estimate , and the proportion with undefined diabetes status at the end of the trial
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 .