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Quantifying Variation in Treatment Utilization for Type 2 Diabetes Across Five Major University of California Health Systems

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posted on 02.02.2021, 19:37 by Thomas A. Peterson, Valy Fontil, Suneil K. Koliwad, Ayan Patel, Atul J. Butte
Objective: Using the newly created University of California Health Data Warehouse (UCHDW), we present the first study to analyze antihyperglycemic treatment utilization across the five large University of California (UC) academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco).

Research Design: Retrospective analysis using deidentified Electronic Health Records (EHRs; 2014-2019) including 97,231 type 2 diabetes patients from 1,003 UC-affiliated clinical settings. Significant differences between health systems and individual providers were identified using binomial probabilities with cohort matching.

Results: Our analysis reveals statistically different treatment utilization patterns not only between health systems but also among individual providers within health systems. We identified 21 differences among health systems, and 29 differences among individual providers within these health systems, with respect to treatment intensifications within existing guidelines on top of either metformin monotherapy or dual therapy with metformin and a sulfonylurea. Next, we identified variation for medications within the same class (e.g., glipizide vs. glyburide among sulfonylureas), with 33 differences among health systems and 86 among individual providers. Finally, we identified two health systems and 55 individual providers that more frequently utilized medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also one health system and 8 providers who prescribed such medications less frequently for these patients.

Conclusions: Our study utilized cohort matching techniques to highlight real-world variation in care between health systems and individual providers. This demonstrates the power of EHRs to quantify differences in treatment utilization, a necessary step towards standardizing precision care for large populations.


Some of the authors were partially supported by the National Institute of General Medical Sciences (NIGMS) grant R01 GM079719 (T.A.P. and A.J.B.), the National Institute of Diabetes and Digestive and Kidney Diseases P30 DK098722 and R01 DK112304 (S.K.K.), and National Heart, Lung, and Blood Institute K23 HL136899 (V.F.). Methods used to deidentify EHR data were supported in part by funding from the National Center for Advancing Translational Sciences of the National Institutes of Health under award number UL1 TR001872. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.