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 .
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
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 .