Lawmakers signal support for using AI to prevent veteran suicides in FY26 VA funding bill reports

House and Senate committee reports accompanying each chamber’s fiscal year 2026 funding bill for the Department of Veterans Affairs encouraged the department to use further innovative tools — such as artificial intelligence — to better identify veterans at high risk of suicide.

The House and Senate passed separate versions of VA’s FY26 funding bill earlier this year. The full-year VA appropriations bill passed Congress last week as part of a package of legislation that included a continuing resolution to end the longest federal government shutdown in U.S. history. 

The FY26 Military Construction and Veterans Affairs bill, signed into law by President Donald Trump on Nov. 12, allocates more than $133 billion in discretionary funding for the department, including over $115 billion for VA medical care. This includes roughly $698 million for VA’s suicide prevention outreach efforts. 

VA has struggled for some time to bring down the overall veteran suicide rate. The department has reported that approximately 6,500 veterans take their own lives each year, with more than 17 retired servicemembers dying by suicide each day — statistics that have remained largely unchanged since 2008, when VA spent around $4.4 million on its suicide prevention initiatives.

As part of an ongoing project, Nextgov/FCW has been reporting on VA’s use and adoption of AI and other emerging capabilities to help direct resources to veterans at risk of self-harm.

While the department has been using some of these tools — including machine learning, a subset of AI — to bolster its outreach to veterans in crisis, both the House and Senate Appropriations committees signaled their support for additional suicide prevention approaches that embrace innovative technologies. The committee reports do not carry the force of law, but they include the recommendations of the panels’ lawmakers. 

The report from the House Appropriations Committee said its members support VA’s current suicide prevention efforts but recognize “there is a significant need to improve early suicide indicators and detection using artificial intelligence and machine learning technologies that improve operational efficiency and effectiveness throughout veteran service delivery.”

The panel’s document added: “To improve veteran service delivery, the Committee encourages the Department to evaluate the use of omnichannel technologies to improve identification of at-risk veterans. The Department may consider using all government service delivery channels with omnichannel capabilities and real-time analytics to ensure that interactions with a veteran can be used to gain appropriate insights that help the Department better identify veterans at-risk in real time and allow for the proper use of resources and decisive actions to be taken.”

The Senate Appropriations Committee’s report similarly expressed support for “predictive Modeling and Analytics for Veterans Suicide Prevention,” adding that “suicide is a complex issue that requires a comprehensive and innovative solution.”

One of the specific tools the Senate panel cited was the Recovery Engagement and Coordination for Health-Veteran Enhanced Treatment — or REACH VET — program. The machine learning tool was launched in 2017 and scans VA’s electronic health records using specific variables to identify veterans in the top 0.1% tier of suicide risk. VA has since updated REACH VET to a 2.0 model that includes new variables, such as military sexual trauma and intimate partner violence, but has also removed race and ethnicity as variables. 

In a press release following passage of the FY26 VA funding bill last week, the office of Sen. Martin Heinrich, D-N.M., wrote that he “secured language encouraging the VA to use predictive modeling and analytics for veteran suicide prevention” and cited the REACH VET program. 

VA has also been working to engage outside organizations and nonprofits to support new uses of AI and other emerging capabilities to prevent veteran suicides, including using AI to train Veteran Crisis Line responders. 

“The Committee is aware of additional predictive data analytics and machine learning tools that may help at-risk veterans before a crisis arises,” the Senate panel’s report said. “The Committee encourages VA to use predictive data analytics and machine learning more broadly across the system to identify veterans with suicidal ideations and better deliver treatment.”

In conversations with Nextgov/FCW over the past year, VA officials, lawmakers and veterans advocates all highlighted the importance of ensuring that these types of tools can augment clinical-led interventions.

Although some media reports, for instance, have expressed concerns about generative AI chatbots being used as therapists or even reportedly being a contributing factor in several suicides, VA has stressed that its capabilities only operate behind the scenes to support providers and are never meant to engage directly with veterans.