January 13, 2026

A-G Associates’ Military, Veteran and Family Center of Excellence
on
Data Driven Suicide Prevention and Outreach Act of 2025

Chairman Miller-Meeks, Ranking Member Brownley, and distinguished Members of the Committee:

We appreciate the opportunity to submit this Statement for the Record in strong support of the bipartisan Data Driven Suicide Prevention and Outreach Act of 2025. This legislation represents a forward-thinking investment in the health and safety of our nation’s veterans by leveraging advanced technology to prevent suicide before a crisis occurs.

The Military, Veterans, and Families Center of Excellence (MVF-COE) is a cross-functional team of researchers, clinicians, strategists, and veterans committed to improving systems of care for service members, veterans, families, and caregivers. Our work is informed by both professional expertise and lived experience navigating the complex realities that can follow separation from service. We have seen firsthand how gaps in care and delayed interventions can lead to devastating outcomes, and we believe this bill offers a critical opportunity to close those gaps.

Veteran suicide remains a national crisis. Despite ongoing efforts, the rate of suicide among veterans continues to exceed that of the civilian population.¹ Current strategies, while essential, often rely on retrospective data or self-reporting, which can limit timely intervention.² To save lives, we must adopt proactive, evidence-based approaches that identify risk before a crisis escalates.

Predictive modeling uses advanced statistical methods, machine learning, and artificial intelligence to analyze large, complex datasets and identify patterns associated with elevated suicide risk. These models can integrate multiple factors, such as health records, demographic

 data, and behavioral indicators, to generate actionable insights.³ By predicting risk trajectories, clinicians and outreach teams can intervene earlier, tailor support to individuals’ needs, and allocate resources more effectively.⁴

Despite progress in suicide prevention, significant gaps remain. Current approaches often depend on veterans seeking help or disclosing suicidal thoughts, which many do not.⁵ Risk assessments are frequently static and fail to account for dynamic changes in health, environment, or behavior.^3,6 Additionally, there is limited integration of predictive analytics into clinical workflows, leaving providers without real-time tools to guide decision-making.⁷

The Data Driven Suicide Prevention and Outreach Act addresses these challenges by funding grants to organizations with expertise in artificial intelligence, data security, and clinical implementation. These grants will enable the development and advancement of predictive models that are clinically actionable, interoperable, and designed to protect veterans’ privacy. ^8,9 By supporting collaboration among nonprofits, academic institutions, and VA, this legislation will accelerate innovation and ensure that life-saving tools reach those who need them most.¹⁰

We commend the bill’s emphasis on evaluation and reporting. To maximize impact, we encourage the incorporation of independent evaluation to ensure objectivity and to measure outcomes beyond technical performance, such as reductions in suicide attempts and improvements in care coordination.¹¹ Transparent, data-driven evaluation will help Congress and VA refine strategies and expand successful interventions.

Additionally, we recommend updating the bill’s language to explicitly require that predictive models be trained not only on clinical and electronic health record data but also on non-medical drivers of health (such as housing instability, employment status, and social support) and military-specific factors (including deployment history, combat exposure, and military sexual trauma). Incorporating these dimensions will improve model accuracy and relevance for the veteran population.^12,13

We urge the Committee to advance the Data Driven Suicide Prevention and Outreach Act of 2025. Passing this legislation would be a decisive step toward fulfilling our nation’s promise to provide veterans with comprehensive, proactive care systems that safeguard their lives and well-being. Thank you for your leadership and unwavering commitment to those who have served.

1. Ramchand, Rajeev, and Tahina Montoya. “Suicide Among Veterans.” RAND Expert Insights (2025). doi:10.7249/PEA1363-1-v2.
2. Adepoju, Adekola George, et al. “AI in Crisis Prediction and Prevention: Leveraging Predictive Analytics for Suicide Risk and Emotional Distress Management.” International Journal of Biological and Pharmaceutical Sciences Archive 10, no. 2 (2025): 49–64. doi:10.53771/ijbpsa.2025.10.2.0078.
3. Bernert, Rebecca A., et al. “Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations.” International Journal of Environmental Research and Public Health 17, no. 5929 (2020). doi:10.3390/ijerph17165929.
4. Kessler, Ronald C., et al. “Developing a Practical Suicide Risk Prediction Model for Targeting High-Risk Patients in the Veterans Health Administration.” International Journal of Methods in Psychiatric Research 26, no. 3 (2017): e1575. doi:10.1002/mpr.1575.
5. Louzon, Samantha A., Robert Bossarte, John F. McCarthy, and Ira R. Katz. “Does Suicidal Ideation as Measured by the PHQ-9 Predict Suicide Among VA Patients?” Psychiatric Services 67, no. 5 (2016): 517–522. doi:10.1176/appi.ps.201500149.
6. Barak-Corren, Yuval, et al. “Validation of an Electronic Health Record–Based Suicide Risk Prediction Modeling Approach Across Multiple Health Care Systems.” JAMA Network Open 3, no. 3 (2020): e201262. doi:10.1001/jamanetworkopen.2020.1262.
7. Yarborough, BobbiJo H., et al. “Clinical Implementation of Suicide Risk Prediction Models in Healthcare: A Qualitative Study.” BMC Psychiatry 22 (2022): 789. doi:10.1186/s12888-022-04400-5.
8. Matarazzo, Bridget B., et al. “The Veterans Health Administration REACH VET Program: Suicide Predictive Modeling in Practice.” Psychiatric Services 74, no. 2 (2023): 206–209. doi:10.1176/appi.ps.202100629.
9. Barnes, Sean M., et al. “Developing Predictive Models to Enhance Clinician Prediction of Suicide Attempts Among Veterans With and Without PTSD.” Suicide and Life-Threatening Behavior 49, no. 4 (2019): 1094–1104. doi:10.1111/sltb.12511.
10. Haroz, Emily E., et al. “Performance of Machine Learning Suicide Risk Models in an American Indian Population.” JAMA Network Open 7, no. 10 (2024): e2439269. doi:10.1001/jamanetworkopen.2024.39269.
11. Collins, Gary S., Paula Dhiman, Jie Ma, Michael M. Schlussel, Lucinda Archer, Ben Van Calster, Frank E. Harrell Jr., Glen P. Martin, Karel G. M. Moons, Maarten van Smeden, Matthew Sperrin, Garrett S. Bullock, and Richard D. Riley. “Evaluation of Clinical Prediction Models (Part 1): From Development to External Validation.” BMJ 384 (2024): e074819. https://doi.org/10.1136/bmj-2023-074819.
12. Busari, Muhammed, and Taiwo Bolanle. “Integrating Social Determinants into Predictive Models for U.S. Public Health Forecasting.” ResearchGate Preprint, May 2025. Accessed January 12, 2026. https://www.researchgate.net/publication/392029463_INTEGRATING_SOCIAL_DETERMINANTS_INTO_PREDICTIVE_MODELS_FOR_US_PUBLIC
13. Meerwijk, Esther L., Andrea K. Finlay, and Alex H. S. Harris. “Retraining the Veterans Health Administration’s REACH VET Suicide Risk Prediction Model for Patients Involved in the Legal System.” npj Mental Health Research 4 (2025): Article 29. doi:10.1038/s44184-025-00143-9.