Thomas A Peterson1, Valy Fontil2,3, Suneil K Koliwad4,5, Ayan Patel6, Atul J Butte7,6. 1. Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA. 2. Division of General Internal Medicine, University of California, San Francisco at Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA. 3. UCSF Center for Vulnerable Populations, San Francisco, CA. 4. Division of Endocrinology, University of California, San Francisco at Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, CA. 5. Diabetes Center and Division of Endocrinology, Department of Medicine, University of California, San Francisco, San Francisco, CA. 6. Center for Data-driven Insights and Innovation, University of California Health, University of California Office of the President, Oakland, CA. 7. Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA atul.butte@ucsf.edu.
Abstract
OBJECTIVE: Using the newly created University of California (UC) Health Data Warehouse, we present the first study to analyze antihyperglycemic treatment utilization across the five large UC academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco). RESEARCH DESIGN AND METHODS: This retrospective analysis used deidentified electronic health records (EHRs; 2014-2019) including 97,231 patients with type 2 diabetes 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 2 health systems and 55 individual providers who more frequently used medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also 1 health system and 8 providers who prescribed such medications less frequently for these patients. CONCLUSIONS: Our study used 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 toward standardizing precision care for large populations.
OBJECTIVE: Using the newly created University of California (UC) Health Data Warehouse, we present the first study to analyze antihyperglycemic treatment utilization across the five large UC academic health systems (Davis, Irvine, Los Angeles, San Diego, and San Francisco). RESEARCH DESIGN AND METHODS: This retrospective analysis used deidentified electronic health records (EHRs; 2014-2019) including 97,231 patients with type 2 diabetes 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 2 health systems and 55 individual providers who more frequently used medications with known cardioprotective benefits for patients with high cardiovascular disease risk, but also 1 health system and 8 providers who prescribed such medications less frequently for these patients. CONCLUSIONS: Our study used 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 toward standardizing precision care for large populations.
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