# A learning health system approach to using Artificial Intelligence Enabled Decision Support (AEDS) for medication optimization in Veteran Care: An Immunosuppressants use case

> **NIH VA I01** · VETERANS HEALTH ADMINISTRATION · 2024 · —

## Abstract

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...

## Key facts

- **NIH application ID:** 10862047
- **Project number:** 1I01HX003749-01A2
- **Recipient organization:** VETERANS HEALTH ADMINISTRATION
- **Principal Investigator:** Jason Ken Hou
- **Activity code:** I01 (R01, R21, SBIR, etc.)
- **Funding institute:** VA
- **Fiscal year:** 2024
- **Award amount:** —
- **Award type:** 1
- **Project period:** 2024-07-01 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10862047

## Citation

> US National Institutes of Health, RePORTER application 10862047, A learning health system approach to using Artificial Intelligence Enabled Decision Support (AEDS) for medication optimization in Veteran Care: An Immunosuppressants use case (1I01HX003749-01A2). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10862047. Licensed CC0.

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