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Evaluation

Suicide and Self-Injury
Session Type:: Symposia

Symposium 30 - Advancing Suicide Risk Assessment for Veterans: Leveraging AI, Assessing Novel Risks, and Developing New Strategies for Risk Management of Veteran Suicide

Saturday, November 22, 2025
10:00 AM - 11:00 AM CST
Location: Celestin G, Level 3
CE: 1

Keywords: Suicide, Risk / Vulnerability Factors, Assessment
Level of Familiarity: Basic to Moderate
Recommended Readings: Litschi, M. A., Lancaster, S. L., Linkh, D. J., Renno, S., Neal‐Walden, T. A., Lawless, C. E., & Breetz, A. A. (2024). Evaluating suicide risk stratification in outpatient settings: A vignette‐based approach. Clinical Psychology & Psychotherapy, 31(2), e2965. https://doi.org/10.1002/cpp.2965,   Edwards, E. R., Fortuna, A., Holliday, R., Addison, H., & Tsai, J. (2024). Prevalence of mental health conditions, substance use disorders, suicidal ideation and attempts, and experiences of homelessness among Veterans with criminal-legal involvement: A meta-analysis. Clinical Psychology Review, 102533. https://doi.org/10.1016/j.cpr.2024.102533,   Matarazzo, B. B., Eagan, A., Landes, S. J., Mina, L. K., Clark, K., Gerard, G. R., ... & Reger, M. A. (2023). The Veterans Health Administration REACH VET program: suicide predictive modeling in practice. Psychiatric services, 74(2), 206-209. https://doi.org/10.1176/appi.ps.2021006,   ,  

United States veterans have significantly greater deaths by suicide than the general population, with 6,392 veteran deaths by suicide reported in 2021, making this the second leading cause of death among veterans under 45 years of age (VA, 2023; Schafer et al., 2022). Early detection is essential for prevention, and screening, education, and active monitoring can reduce suicide attempts (Matarazzo et al., 2023; Nelson et al., 2017). Despite substantial efforts, suicide rates among veterans continue to rise, highlighting the need for innovative prevention strategies.

This symposium provides an overview of innovative suicide risks assessment strategies leveraged to identify veterans in need of suicide prevention. Presentations will describe the use of advanced artificial intelligence applications to identify suicide risks in veterans. Other presentations will describe the use of large data to identify suicide risks faced by veterans with diverse life experiences. Another presentation will describe the use of a mixed methods approach to highlight the significant impact of social determinants of health and trauma histories on veterans. Interviews highlight the benefits of integrating an elevated screening pathway for suicide assessment with case management. The presentations are unique in their coverage of a wide range of strategies and veteran characteristics as a means of facilitating veteran suicide prevention.

The first presentation uses data from veterans who accessed VHA services from 2020-2024 to build a suicide risk prediction algorithm based on actuarial suicide ratings, semistructured suicide interview, and medical chart information to identify risks of suicide for veterans with experienced criminal justice involvement or who have experienced homelessness. It is expected that the suicide risks for these veterans will be distinct from the broader veteran population.

The second presentation assesses the use of generative artificial intelligent-large language models’ (GAI-LLMs) ability to assess acute and chronic suicide risks and make treatment recommendations for veterans. The GAI-LLMs assessment of acute and chronic risks are compared to human mental health care providers’ ratings using the same veteran cases.

The third presentation describes a mixed method investigation assessing factors relating to suicide risk among military-connected clients, highlighting the significant impact of social determinants of health and trauma histories, particularly among gender and ethno-racial minorities. Insights from former clients emphasized the importance of effective care approaches in risk assessment and management, underscoring the value of care model that includes an elevated risk pathway and integrated case management.

A discussant will integrate findings based on their extensive experience in veteran care, suicide prevention, and implementation science. They will offer recommendations on utilizing technology and engaging veterans in suicide prevention to enhance outcomes.

These findings from these presentations have substantial public health ramifications as the identification of unique suicide risks can lead to improved strategies for identification and mitigation of deaths by suicide.

Learning Objectives:

At the end of this session, the learner will be able to:
  1. Describe the effectiveness of generative artificial intelligence-large language models in assessing acute and chronic suicide risks.
  2. Explain the unique suicide risk factors associated with veterans who have experienced homelessness and criminal justice system.
  3. Describe the concept of an elevated risk pathway for suicide risk assessment for veterans with moderate to high suicide risks.

Presentations:

  • 10:03 AM - 10:22 AM CST
    (SYM 30) Suicide Risk Prediction Modeling in Veterans Experiencing Homelessness And/or Criminal-legal Involvement

    Speaker: Emily R. Edwards, Ph.D. (she/her/hers) – US Department of Veterans Affairs; Icahn School of Medicine at Mount Sinai

    Co-author: Ryan Holliday, PhD (he/him/his) – US Department of Veterans Affairs

  • 10:22 AM - 10:41 AM CST
    (SYM 30) Effectiveness of Generative Ai-large Language Models' Recognition of Veteran Suicide Risk: A Comparison with Human Mental Health Providers Using a Risk Stratification Model

    Speaker: Alana Encalade, M.A. (she/her/hers) – University of Houston – Clear Lake

    Co-author: Randee M. Schmitt, M.A. (she/her/hers) – University of Houston – Clear Lake

    Co-author: Bre Wuckovich – UHCL

    Co-author: Natashaa A. Dalal, B.S., M.A. – University of Houston – Clear Lake

    Co-author: Hela Desai, B.S. – University of Houston – Clear Lake

    Co-author: Shealyn Tomlinson – UHCL

    Co-author: Sean A. Lauderdale, Ph.D. (he/him/his) – University of Houston – Clear Lake

  • 10:41 AM - 11:00 AM CST
    (SYM 30) Enhancing Suicide Risk Assessment and Prevention Among Military-connected Clients: Insights from a Mixed-methods Study

    Speaker: Melissa A. Litschi, Ph.D. (she/her/hers) – Cohen Veterans Network

    Co-author: Steven L. Lancaster, Ph.D. – Cohen Veterans Network

    Co-author: David J. Linkh, Ph.D., LCSW – Cohen Veterans Network