There was mixed evidence that provider-level computerized decision support improves outcomes .
There was more evidence in support of patient-level tools that provide connectivity to the healthcare system , as well as system-level interventions that involve data base monitoring and outreach by central ized care teams .
R and omized controlled trials show wide variability in the effects of HIT on lipid management outcomes .
Evidence suggests that multilevel HIT approaches that target not only providers but include patients and systems approaches will be needed to improve lipid treatment , adherence and quality
BACKGROUND Large gaps in lipid treatment and medication adherence persist in high-risk out patients in the United States .
Health information technology ( HIT ) is being applied to close quality gaps in chronic illness care , but its utility for lipid management has not been widely studied .
OBJECTIVE To perform a qualitative review of the impact of HIT interventions on lipid management processes of care ( screening or testing ; drug initiation , titration or adherence ; or referrals ) or clinical outcomes ( percent at low density lipoprotein cholesterol goal ; absolute lipid levels ; absolute risk scores ; or cardiac hospitalizations ) in out patients with coronary heart disease or at increased risk .
Combined Task Delegation, Computerized Decision Support, and Feedback Improve Cardiovascular Risk for Type 2 Diabetic Patients
OBJECTIVE —The Diabetes Care Protocol combines task delegation ( a practice nurse ) , computerized decision support , and feedback every 3 months . We studied the effect of the Diabetes Care Protocol on A1C and cardiovascular risk factors in type 2 diabetic patients in primary care . RESEARCH DESIGN AND METHODS —In a cluster r and omized trial , mean changes in cardiovascular risk factors between the intervention and control groups after 1 year were calculated by generalized linear models . RESULTS —Throughout the Netherl and s , 26 intervention practice s included 1,699 patients and 29 control practice s 1,692 patients . The difference in A1C change was not significant , whereas total cholesterol , LDL cholesterol , and blood pressure improved significantly more in the intervention group . The 10-year coronary heart disease risk estimate of the UK Prospect i ve Diabetes Study improved 1.4 % more in the intervention group . CONCLUSIONS —Delegation of routine diabetes care to a practice nurse combined with computerized decision support and feedback did not improve A1C but reduced cardiovascular risk in type 2 diabetes patients
There was mixed evidence that provider-level computerized decision support improves outcomes .
There was more evidence in support of patient-level tools that provide connectivity to the healthcare system , as well as system-level interventions that involve data base monitoring and outreach by central ized care teams .
R and omized controlled trials show wide variability in the effects of HIT on lipid management outcomes .
Evidence suggests that multilevel HIT approaches that target not only providers but include patients and systems approaches will be needed to improve lipid treatment , adherence and quality
BACKGROUND Large gaps in lipid treatment and medication adherence persist in high-risk out patients in the United States .
Health information technology ( HIT ) is being applied to close quality gaps in chronic illness care , but its utility for lipid management has not been widely studied .
OBJECTIVE To perform a qualitative review of the impact of HIT interventions on lipid management processes of care ( screening or testing ; drug initiation , titration or adherence ; or referrals ) or clinical outcomes ( percent at low density lipoprotein cholesterol goal ; absolute lipid levels ; absolute risk scores ; or cardiac hospitalizations ) in out patients with coronary heart disease or at increased risk .