• S F Dinneen, S S Bjornsen, S C Bryant, B R Zimmerman, C A Gorman, J B Knudsen, R A Rizza, and S A Smith.
• Division of Endocrinology, Nutrition and Metabolism, Mayo Clinic, Rochester, MN 55905, USA.
• J Eval Clin Pract. 2000 Nov 1; 6 (4): 421-9.

AbstractThe objective of the Mayo Health System Diabetes Translation Project is to assess the impact of three different models of care on the overall quality of diabetes care in the community. The unit of study is the primary care practice with a different model of care implemented at each of three sites. The design incorporates a comparison of a diabetes guideline implementation team initiative (Practice model A), a guideline initiative combined with clinical use of a Diabetes Electronic Management System (DEMS) by primary care providers (Practice model B) and a guideline initiative combined with DEMS utilization combined with electronic review of DEMS patient encounters by an endocrinologist (Practice model C). Administrative data sets were used to define the patient population at each practice. Patients were designated as new, attending or non-attending based on their pattern of visits over the preceding 12 months. A random sample of 200 charts from attending patients at each site was audited at baseline for diabetes-related process and outcome measures. This audit will be repeated yearly during the 2 years of the project. Baseline data revealed significant differences across sites in adherence to certain key indicators of the quality of diabetes care including: frequency of documentation of eye examinations (19, 39 and 37% for sites A, B and C, respectively), haemoglobin A1c monitoring (64, 89 and 77%) and microalbumin monitoring (3, 15 and 6%). The interventions being assessed in this study include traditional (diabetes education; guideline implementation) and modern (DEMS; telemedicine specialist review) methods for improving the quality of diabetes care. In spite of variation in baseline quality indicators, the setting and design should lead to broad applicability of the results and help determine an optimal model of diabetes care in the community.

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