Background: In the modern era of drug development, drug manufacturers are regularly introducing novel medications that require a wide range of resources that may increase the burden for Veterans seeking care. The VHA must consider how to provide high-quality care in the context of competing resources, the incremental benefit of novel medications over established treatments, and the needs of Veterans. There is a critical need and opportunity to optimize treatment with established medications to maximize their benefit and provide high-quality care considering competing VHA resources and Veterans’ needs. Significance: This challenge is exemplified in the treatment of inflammatory bowel disease (IBD), a costly and debilitating lifelong disease with rising resource needs. IBD represents an ideal proof-of-concept and real-world example on which to conduct research to address: 1. Patient-centered treatment optimization by putting VA Data to Work for Veterans using novel AI/ML methods, 2. Pre-implementation strategies for adoption, and 3. Optimal approaches to healthcare utilization (e.g., access to care) using the VA as a Learning Healthcare System–All of which are VA HSR&D Priorities, updated 3/2023. Innovation & Impact: IBD consists of Crohn’s disease and ulcerative colitis (UC) and affects nearly 3 million Americans and ~80,000 Veterans. This study addresses several key gaps in the care of Veterans with IBD by: Adapting and validating prediction models for optimizing thiopurines; Assessing the usability, acceptability, and feasibility of an Artificial Intelligence (AI)/Machine Learning (ML)-enabled decision-support systems (AEDS) for thiopurine optimization in the VHA; and Evaluating the impact of an AEDS-optimized thiopurine treatment policy on clinical outcomes. Ultimately, an AEDS for thiopurine optimization in Veterans will provide a pragmatic and objective guide for providers to choose to continue, stop, or modify the course of an IBD therapy to produce optimal outcomes while maximizing resources in IBD. Specific Aims: Using learning health system framework, we will: Aim 1: Adapt and validate AI/ML-based models to predict inadequate immunosuppression in Veterans with IBD using real- world data from the VHA EHR (Data-to-Knowledge). Aim 2: Assess the usability, acceptability, and feasibility of AEDS for thiopurine optimization in Veterans with IBD (Knowledge-to-Practice). Aim 3: To evaluate an AEDS- based treatment policy vs. usual care on clinical outcomes (Practice-to-Data). Methodology: In Aim 1, we will retrospectively identify all Veterans with a diagnosis of IBD on thiopurines in the last 10 years from the VHA Corporate Data Warehouse. We will adapt and externally validate our previous model among Veterans with moderate-severe UC on thiopurine monotherapy and internally validate a new model for Veterans with IBD on combination therapy (thiopurines + anti-TNF biologics). We will use AI/ML-based techniques to assess the performance characteris...