Project Summary Since 2007, more than 40 glucose-lowering drugs (GLDs) have been approved by the US Food and Drug Administration to treat diabetes. These newer GLDs have been proven to have higher cardiorenal benefits than older classes when applied in people at high risk of cardiovascular and kidney disease. However, the introduction of these high-cost GLDs has led to significant quality and equity concerns in diabetes care: socially disadvantaged individuals tend to have limited access to newer GLDs due to barriers related to social attributes (e.g., income, education), resulting in gaps and disparities in achieving optimal health outcomes. There is, therefore, an urgent need to improve the quality of care and equity in using newer GLDs among millions of Americans living with type 2 diabetes (T2D). Previous studies have found that programs that improve the quality of care by promoting treatment in targeted clinically high-benefit user groups lead to equity improvement because high-benefit users from socially disadvantaged subgroups often have larger gaps in care thus benefit more from these programs. However, critical knowledge gaps exist in identifying the clinically high-benefit users of newer GLDs and designing policy- level interventions that can adequately motivate patients’ newer GLDs use while having good long-term health and economic outcomes. Thus, the OBJECTIVE of this proposed project is to identify clinically high-benefit T2D patient subgroups for newer GLDs and generate empirical economic evidence for designing policy-level interventions to improve the quality of care and health equity in T2D care. High-quality comparative effectiveness research (CER) requires the patients to have complete data records which can track event encounters and treatment exposure with high accuracy. These individuals were often referred to as “loyal patients.” In this proposed project, we will develop a computable phenotype (CP) for “loyal patients” using OneFlorida EHRs and cross-network validate the CP using REACHnet EHRs (Aim 1). To identify clinically high-benefit T2D patient subgroups for newer GLDs, we will conduct comparative effectiveness and safety analyses of newer GLDs versus guideline-recommended alternatives across patient subgroups using rigorous causal inference methods and a machine-learning (ML) approach. The high-benefit T2D patient subgroups will be identified using EHRs of “loyal patients” from OneFlorida and cross-validated in REACHnet (Aim 2). At last, we will evaluate the impact of potential policy-level interventions for promoting newer GLDs use in high-benefit users on health, economics, and equity outcomes. Leveraging an advanced ML algorithm developed by PI, we will also identify the ideal cost-sharing structure at a health-plan level to maximize drug adherence while reducing the payers' burden. The proposed research is significant because it will provide solutions for an emergent public health issue in quality of care and heal...