Observational analysis reveals AI-driven risk prediction models in Veterans, suggesting improvements in healthcare value.
Objective: By personalizing healthcare to an individual's specific requirements, precision health promises to maximize benefit and minimize harm, thereby maximizing value. We describe here, how in Phase 2 of the Million Veteran Program Computational Health Analytics for Medical Precision to Improve Outcomes Now (MVP CHAMPION), artificial intelligence (AI) and high performance computing (HPC) have been applied to Veteran's electronic health records (EHRs) and genetic data to advance real world precision health. Materials and Methods: Eight concept projects were selected on the basis of potential impact on high burden conditions among Veterans, including heart failure, suicide, lung cancer, diabetes, post COVID 19 sequelae, medication toxicity, and obstructive sleep apnea. Results: Achievements include new and more discriminating risk prediction models to inform medical decision making, multimorbidity aware analytic frameworks, and development and deployment of reusable computational tools. The identification of novel risk factors from genetic data and unstructured text in the EHR has both informed risk prediction and offered new insights for medication repurposing and development. Discussion: We not only confirmed the need for shared infrastructure, data management, and novel AI based workflows to inform precision health, but also found that such programmatic improvements result in valuable mechanistic insights. Conclusion: By building on these foundations through expanded deployment, adaptive modeling, and broader partnerships, the VA DOE collaboration is poised to transform not only the future of Veterans' healthcare, but the broader national landscape of precision health.
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Justice et al. (2025) studied this question.
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