Correlation between provider use rates of a clinical decision support tool and diabetes performance measures [e-poster 1266-P] Conference Poster uri icon

abstract

  • A previously published randomized controlled trial demonstrated that implementation of an electronic health record-linked personalized clinical decision support (PCDS) tool within primary care clinics improved mean A1c and BP control. We subsequently implemented a modification of the CDS for expanded use with high cardiovascular risk adults (CV-PCDS) that also retained the decision support for glycemic control for patients with diabetes. Here we analyze the association between primary care provider use rates of CV-PCDS with diabetes performance measures in patients with diabetes. Using data from a cluster randomized trial in 2012-2014, we analyzed the association of CV-PCDS provider-specific use rates in March 2014 with diabetes performance measures 6 months later, using Pearson correlation coefficients. Performance measures included the proportion of a provider’s diabetes patients who (a) achieved A1c < 8%, and (b) achieved a composite measure of optimal diabetes care (ODC) that required simultaneous achievement of A1c < 8%, SBP < 140 mm Hg, LDL < 100 mg/dl, non-tobacco user, and ASA use for secondary prevention. Providers (N=43) used the CV-PCDS tool at a mean of 82.1% of targeted encounters of adults with high CV risk (range across providers 36.0% to 100% of encounters). The mean percentage of the diabetes subgroup who achieved A1c <8% was 73.7%, and the percentage of patients who achieved the ODC goal was 46.8%. Pearson correlation coefficients between March 2014 CV-PCDS provider use rates and A1c and ODC performance measures in August 2014 were 0.16 (p= 0.31) and 0.24 (p= 0.12) respectively. In this high-performing health care system with high CV-PCDS use rates, there was a positive but non-significant association of provider use of the CV-PCDS tool and provider-level quality of diabetes care 6 months later. The generalizability of this finding to lower-performing care systems, and to providers with lower baseline quality of diabetes care remains to be determined.

publication date

  • 2016