In addition , the more CCM-incorporated the information technology-based interventions were , the more improvements there were in HbA1c levels .
CONCLUSIONS Information technology strategies combined with the other elements of chronic care models are associated with improved glycemic control in people with diabetes .
No clinical ly relevant impact was observed on low-density lipoprotein levels and blood pressure , but there was evidence that the cost of care was lower
BACKGROUND Information technology-based interventions are increasingly being used to manage health care .
However , there is conflicting evidence regarding whether these interventions improve outcomes in people with type 2 diabetes .
OBJECTIVE The objective of this study was to conduct a systematic review and meta- analysis of clinical trials , assessing the impact of information technology on changes in the levels of hemoglobin A1c ( HbA1c ) and mapping the interventions with chronic care model ( CCM ) elements .
The Vermont Diabetes Information System: A Cluster Randomized Trial of a Population Based Decision Support System
BACKGROUND Optimal care for patients with diabetes is difficult to achieve in clinical practice . OBJECTIVE To evaluate the impact of a registry and decision support system on processes of care , and physiologic control . PARTICIPANTS R and omized trial with clustering at the practice level , involving 7,412 adults with diabetes in 64 primary care practice s in the Northeast . INTERVENTIONS Provider decision support ( reminders for overdue diabetes tests , alerts regarding abnormal results , and quarterly population reports with peer comparisons ) and patient decision support ( reminders and alerts ) . MEASUREMENTS AND MAIN RESULTS Process and physiologic outcomes were evaluated in all subjects . Functional status was evaluated in a r and om patient sample via question naire . We used multiple logistic regression to quantify the effect , adjusting for clustering and potential confounders . Intervention subjects were significantly more likely to receive guideline -appropriate testing for cholesterol ( OR = 1.39 ; [ 95%CI 1.07 , 1.80 ] P = 0.012 ) , creatinine ( OR = 1.40 ; [ 95%CI 1.06 , 1.84 ] P = 0.018 ) , and proteinuria ( OR = 1.74 ; [ 95%CI 1.13 , 1.69 ] P = 0.012 ) , but not A1C ( OR = 1.17 ; [ 95 % CI 0.80 , 1.72 ] P = 0.43 ) . Rates of control of A1C and LDL cholesterol were similar in the two groups . There were no differences in blood pressure , body mass index , or functional status . CONCLUSIONS A chronic disease registry and decision support system based on easily obtainable laboratory data was feasible and acceptable to patients and providers . This system improved the process of laboratory monitoring in primary care , but not physiologic control
In addition , the more CCM-incorporated the information technology-based interventions were , the more improvements there were in HbA1c levels .
CONCLUSIONS Information technology strategies combined with the other elements of chronic care models are associated with improved glycemic control in people with diabetes .
No clinical ly relevant impact was observed on low-density lipoprotein levels and blood pressure , but there was evidence that the cost of care was lower
BACKGROUND Information technology-based interventions are increasingly being used to manage health care .
However , there is conflicting evidence regarding whether these interventions improve outcomes in people with type 2 diabetes .
OBJECTIVE The objective of this study was to conduct a systematic review and meta- analysis of clinical trials , assessing the impact of information technology on changes in the levels of hemoglobin A1c ( HbA1c ) and mapping the interventions with chronic care model ( CCM ) elements .