    [House Hearing, 119 Congress]
    [From the U.S. Government Publishing Office]

                           ADVANCING VA CARE THROUGH
                            ARTIFICIAL INTELLIGENCE

    =======================================================================

                                    HEARING

                                   BEFORE THE

                            SUBCOMMITTEE ON TECHNOLOGY
                                   MODERNIZATION

                                     OF THE

                         COMMITTEE ON VETERANS' AFFAIRS

                         U.S. HOUSE OF REPRESENTATIVES

                        ONE HUNDRED NINETEENTH CONGRESS

                                 FIRST SESSION

                                   __________

                           MONDAY, SEPTEMBER 15, 2025

                                   __________

                               Serial No. 119-34

                                   __________

           Printed for the use of the Committee on Veterans' Affairs

    [GRAPHIC NOT AVAILABLE IN TIFF FORMAT]

                        Available via http://govinfo.gov

                                    __________

                       U.S. GOVERNMENT PUBLISHING OFFICE
    61-916                  WASHINGTON : 2025

    -----------------------------------------------------------------------------------

                         COMMITTEE ON VETERANS' AFFAIRS

                         MIKE BOST, Illinois, Chairman

    AUMUA AMATA COLEMAN RADEWAGEN,       MARK TAKANO, California, Ranking
        American Samoa, Vice-Chairwoman      Member
    JACK BERGMAN, Michigan               JULIA BROWNLEY, California
    NANCY MACE, South Carolina           CHRIS PAPPAS, New Hampshire
    MARIANNETTE MILLER-MEEKS, Iowa       SHEILA CHERFILUS-MCCORMICK,
    GREGORY F. MURPHY, North Carolina        Florida
    DERRICK VAN ORDEN, Wisconsin         MORGAN MCGARVEY, Kentucky
    MORGAN LUTTRELL, Texas               DELIA RAMIREZ, Illinois
    JUAN CISCOMANI, Arizona              NIKKI BUDZINSKI, Illinois
    KEITH SELF, Texas                    TIMOTHY M. KENNEDY, New York
    JEN KIGGANS, Virginia                MAXINE DEXTER, Oregon
    ABE HAMADEH, Arizona                 HERB CONAWAY, New Jersey
    KIMBERLYN KING-HINDS, Northern       KELLY MORRISON, Minnesota
        Mariana Islands
    TOM BARRETT, Michigan

                           Jon Clark, Staff Director
                      Matt Reel, Democratic Staff Director

                    SUBCOMMITTEE ON TECHNOLOGY MODERNIZATION

                        TOM BARRETT, Michigan, Chairman

    NANCY MACE, South Carolina           NIKKI BUDZINSKI, Illinois, Ranking
    MORGAN LUTTRELL, Texas                   Member
                                         SHEILA CHERFILUS-MCCORMICK,
                                             Florida

    Pursuant to clause 2(e)(4) of Rule XI of the Rules of the House, public
    hearing records of the Committee on Veterans' Affairs are also
    published in electronic form. The printed hearing record remains the
    official version. Because electronic submissions are used to prepare
    both printed and electronic versions of the hearing record, the process
    of converting between various electronic formats may introduce
    unintentional errors or omissions. Such occurrences are inherent in the
    current publication process and should diminish as the process is
    further refined.

                             C  O  N  T  E  N  T  S

                                  ----------

                           MONDAY, SEPTEMBER 15, 2025

                                                                       Page

                               OPENING STATEMENTS

    The Honorable Tom Barrett, Chairman..............................     1
    The Honorable Nikki Budzinski, Ranking Member....................     3

                                   WITNESSES
                                    Panel I

    Mr. Charles Worthington, Chief Technology Officer & Chief
      Artificial Intelligence Officer, Office of Information &
      Technology, U.S. Department of Veterans Affairs................     5

            Accompanied by:

        Dr. Evan Carey, Ph.D., Acting Director, National Artificial
            Intelligence Institute, Digital Health Office, Veterans
            Health Administration, U.S. Department of Veterans
            Affairs

    Mr. Sid Ghatak, Chief Technical Advisor, National Artificial
      Intelligence Association.......................................     7

    Dr. Mohammad Ghassemi, Ph.D., Assistant Professor, Department of
      Computer Science and Engineering, College of Engineering,
      Michigan State University......................................     9

    Ms. Carol Harris, Director, Information Technology and
      Cybersecurity Issues, U.S. Government Accountability Office....    10

                                    APPENDIX
                        Prepared Statements Of Witnesses

    Mr. Charles Worthington Prepared Statement.......................    31
    Mr. Sid Ghatak Prepared Statement................................    32
    Dr. Mohammad Ghassemi, Ph.D. Prepared Statement..................    35
    Ms. Carol Harris Prepared Statement..............................    40

                           ADVANCING VA CARE THROUGH
                            ARTIFICIAL INTELLIGENCE

                                  ----------

                           MONDAY, SEPTEMBER 15, 2025

      Subcommittee on Technology Modernization,
                        Committee on Veterans' Affairs,
                                 U.S. House of Representatives,
                                                        Washington, DC.
        The subcommittee met, pursuant to notice, at 2:58 p.m., in
    room 360, Cannon House Office Building, Hon. Tom Barrett
    (chairman of the subcommittee) presiding.
        Present: Representatives Barrett, Luttrell, Budzinski, and
    Cherfilus-McCormick.

               OPENING STATEMENT OF TOM BARRETT, CHAIRMAN

        Mr. Barrett. The subcommittee will come to order.
        Without objection, the chair may declare a recess at any
    time.
        Like many who have worn the uniform and received U.S.
    Department of Veterans Affairs (VA) health care, I know the
    frustration when the system is slow, the paperwork stacks up or
    the technology fails, or does not lead us in the direction we
    are trying to go. That is why this subcommittee's work is so
    critical and why it is important that we have the folks here
    joining us today.
        It is our duty to ensure VA's technology is efficient and
    reliable, helping veterans rather than standing in the way of
    their care. That brings us to the focus of today's hearing,
    artificial intelligence, or AI, as it is of course commonly
    referred to right now. For some, AI sounds like a science
    fiction movie--we have all seen many of them--something only
    computer scientists worry about or even something scary because
    it is unknown and not well understood. It feels like today
    everything is about drones or artificial intelligence. The
    world has shifted quite a bit.
        Within VA, AI is already being used in ways they are making
    a real difference for our veterans. In fact, U.S. Government
    Accountability Office (GAO) recently released a report
    highlighting how VA is among the most active adopters of
    artificial intelligence, from analyzing medical images and
    workflows to creating summary diagnostic reports. The report
    identified more than 200 reported use cases across the system.
    As we were preparing for this hearing, my staff had told me
    about some, even very early prototype AI systems that the VA
    had integrated decades ago.
        In clinical care, AI can help doctors detect cancer earlier
    and identify warning signs of heart disease before a crisis
    occurs.
        A recent study led by VA researchers at the VA Long Beach
    Health Care System showed how AI can enable providers to detect
    the risk of calcium buildup in heart arteries. This new
    technology could give providers the chance to prevent heart
    attacks rather than respond to them.
        AI is also being used to enhance mental healthcare. One of
    the greatest challenges we face as a Nation remains around
    veteran suicide. In 2017, VA launched the react vet program--or
    the Recovery Engagement and Coordination for Health Veterans
    Enhanced Treatment (REACH VET) program. This program uses an AI
    model to help identify a very small group of veterans who are
    at the greatest risk of suicide. The results were promising.
    The program helped VA step in early, guiding veterans to care
    before crisis strikes.
        It is not just about medical breakthroughs. AI is also
    helping doctors and nurses relieve the day-to-day burden of
    paperwork and things that are not spending time with the
    patients. The most common complaint from providers is that they
    spend too much time filling forms and not enough time taking
    care of their patients.
        The VA is exploring AI tools, like Ambient scribes, which
    can listen to a provider's conversation with the patient and
    automatically create a clean and accurate medical note. On
    average, this saves providers 2 or 3 hours per week. Now
    multiply that across thousands of staff. It means more time
    spent caring for veterans and less time staring at a computer
    screen and doing paperwork. The promise of AI is real. I want
    to be clear: Our job here is not applaud the promise. It is to
    make sure that AI is being used responsibly, safely, and
    transparently. Every great innovation comes with risk, and AI
    is of course no exception. If the data is biased, the results
    can be unfair. If safeguards are weak, privacy is compromised.
    If the systems are not carefully monitored, mistakes could harm
    the very people they are trying to protect. That is why the
    governance of AI matters.
        The VA has been one of the first agencies in the government
    to step up a framework for how AI should be reviewed and
    approved. By law, VA already has some of the strongest privacy
    protections in government, and those protections extend
    directly to AI technology in veterans health and benefits, that
    it cannot be used by vendors for other purposes, period. We are
    going to hear more about that today.
        Veterans deserve to know when AI is being used in their
    care. They deserve to know the technology has been tested and
    that it is working for them, not against them. Congress
    deserves to see evidence that taxpayer dollars are being well-
    spent.
        I see today's hearing as an opportunity to highlight what
    is working, to dig into what still needs improvement, and to
    set clear expectations for the road ahead.
        AI does not and will not replace doctors or nurses, nor the
    human touch that every veteran deserves when receiving their
    care. Done right, AI can give clinicians another tool in their
    tool box, helping them focus more on patients and ultimately on
    saving lives.
        We need to find the balance between moving efficiently
    enough to give veterans the benefit of innovation and
    cautiously enough to make sure that no veteran is put at risk.
    Our veterans should never be guinea pigs for untested
    technology, but they should also not be denied the benefits of
    safe and proven innovations.
        This subcommittee will hold VA to that standard, and I
    intend to make sure that we get it right. It is not about
    technology; it is about trust. Veterans give this Nation their
    trust through their service. When they in turn go to the VA,
    they deserve to know that trust will be given back and honored.
    It is our duty to make sure that that trust is never broken. I
    know the ranking member will join me in this pursuit, and I
    appreciate your joining me in this committee today.
        Then, last, before I yield to her, I sat next to another
    physician on my flight here last night. We were talking about
    AI and actually about this hearing coming up. She was saying,
    ``We are never going to replace the element of patient care
    that is done by doctors, nurses, and other medical
    professionals.'' If we can expand the reach that they have,
    look at it as, in the Army, we used to say a force multiplier
    to deliver benefits in a more deliberative fashion that
    benefits everybody. That is what we ought to be pursuing here.
        With that, I will yield to Ranking Member Budzinski for
    your opening statement.

          OPENING STATEMENT OF NIKKI BUDZINSKI, RANKING MEMBER

        Ms. Budzinski. Thank you very much, Chairman Barrett. I
    appreciate our subcommittee coming together to have a frank
    conversation about the underlying Information Technology (IT)
    challenges at the Department of Veterans Affairs and how we can
    support the VA in closing those gaps through technology.
        However, today's review of artificial intelligence use
    cases at the Veterans Health Administration (VHA) feels like a
    distraction. VA is struggling with the basics. We are here
    discussing the newest technologies while the VA is still
    working with a crumbling IT infrastructure and still grapples
    to modernize systems and workflows.
        As the ranking member on the Technology Modernization
    Subcommittee, I am certainly excited by the potential of both
    AI and innovation. AI could improve some of VA's challenges
    through large language models and higher processing speeds. We
    have seen promising studies of providers using AI to identify
    cancers more easily, improve patient outcomes, and ease
    clinician burnout by taking on more administrative tasks.
        The VA has certainly been the leader in the research and
    development and widespread usage of a number of significant and
    groundbreaking technologies. It stands to do so again with AI.
    However, success in these efforts requires adequate resources
    and investments in its budgets, its processes, and its people.
    Veterans choose VA for the community it provides, the people it
    employs, and for the fact that it is not driven by profit.
        What VA does best is make veterans feel seen and
    understood. As we have seen, AI can be a tool to provide
    decision support, ease provider burdens, and help with
    notetaking so doctors can be more present with the patient. We
    should also acknowledge that it is not the answer to every
    challenge the VA faces. Also, we as a committee and as Congress
    need to have a real conversation about AI policy and how to
    implement it safely. I am excited about the opportunities that
    AI presents. I am not convinced that VA is prepared to deploy
    this technology just yet.
        I have a number of concerns that I hope to address today,
    like the lack of regulation and governance structures and the
    need for better transparency around what data is involved in
    training such models.
        Further, like all technology modernization efforts,
    implementing AI successfully requires a highly skilled,
    adequately staffed workforce. Almost 2 weeks ago, the acting
    head of the Department on Government Efficiency stressed the
    need to ``hire and empower great tech talent in government.'' I
    could not agree more with that. However, I think we should all
    note the irony of that statement considering Office of
    Information and Technology (OIT) is proposing a massive
    reorganization and intends to cut at least 20 percent of its
    workforce.
        Success is also reliant on strong IT leadership. If OIT is
    in fact undergoing significant changes to its organizational
    structure, priorities list, and workforce makeup, we need a
    confirmed Chief Information Officer (CIO) at VA. This position
    is particularly critical as we see the acceleration and
    progression of modernization efforts at the Department. It
    seems the VA still lacks a coherent enterprise IT strategy,
    leaving projects AI integration to happen in silos. Without
    stable and competent leadership, veterans and VA employees will
    continue to be stuck with cobbled-together systems and
    workflows that do not meet their needs rather than a solid
    strategy for technology usage to guide its decision-making.
        I hope that we can get some clarity into the
    administration's plan to propose a nominee for the CIO position
    and that one can be confirmed before many of these substantial
    changes occur.
        Last, I understand this subcommittee held a similar hearing
    in January 2024, though neither I nor the chairman were on this
    subcommittee at that point. In that hearing, data privacy was
    an intrinsic part of the discussion. I hope that it still is
    the case today.
        As we become more interconnected through technology
    advancements like artificial intelligence, we must become
    increasingly aware of the concerns about the privacy of users'
    data, especially in healthcare. Since this last hearing, the
    Department has been entangled in multiple cybersecurity
    incidents, which have potentially placed veterans' data at
    risk. Though many of these breaches have been targeted at VA
    contractors, veterans' data has still been implicated, and VA
    maintains some responsibility for its safety. Though I do feel
    that this hearing is perhaps too early, considering VA has yet
    to develop and release some of its policies and plans to align
    its efforts with the administration's, I hope to hear from our
    VA witnesses today about how data privacy and security, as well
    as the views of both VA employees and patients, will be
    integrated into such plans.
        Thank you and I yield back, Mr. Chairman.
        Mr. Barrett. Thank you, Ranking Member Budzinski.
        I join you in making sure that we have adequate ethics
    guardrails around this, and certainly privacy is paramount in
    that as well.
        I now want to introduce our witnesses. Again, thank you for
    joining us today from the Department of Veterans Affairs, we
    have Mr. Charles Worthington, the Chief Technology Officer and
    Chief Artificial Intelligence Officer. Thank you for being
    here. Accompanying Mr. Worthington is Dr. Evan Carey, Acting
    Director over the National Artificial Intelligence Institute at
    the VA. We also have Mr. Sid Ghatak.
        Did I say that correctly?
        Mr. Ghatak. Yes, sir.
        Mr. Barrett. Thank you. The chief technical advisor from
    the National Artificial Intelligence Association. Dr. Mohammad
    Ghassemi, assistant professor at Michigan State University. Go
    green.
        Dr. Ghassemi. Go white.
        Mr. Barrett. Thank you for being here today as well.
        Finally, from the Government Accountability Office we have
    Ms. Carol Harris, a familiar face to all of us on this
    committee. Thank you again for being here and joining us. She
    is also Director of IT and Cybersecurity at the GAO. Again,
    thank you all for being here.
        At this time, I ask the witnesses to please stand and raise
    your right-hand.
        [Witnesses sworn.]
        Mr. Barrett. Thank you. Let the record reflect that all
    witnesses have answered in the affirmative.
        Mr. Charles Worthington, you are now recognized for 5
    minutes to deliver your opening statement on behalf of VA.

                    STATEMENT OF CHARLES WORTHINGTON

        Mr. Worthington. Chairman Barrett, Ranking Member
    Budzinski, and distinguished members of the subcommittee, thank
    you for the opportunity to discuss the Department of Veterans
    Affairs' use of artificial intelligence to enhance healthcare
    and services for veterans.
        Your steadfast support of the veterans and their families
    is invaluable. I am joined today by Dr. Evan Carey, Acting
    Director of the National AI Institute in the Digital Health
    Office of the Veterans Health Administration.
        While AI is not new to VA, recent advancements in AI
    systems presents a tremendous opportunity to improve VA's
    services. When used effectively, AI can improve the efficiency
    and accuracy of many time-consuming and error-prone tasks that
    create burdens for VA staff and veterans alike. That is why VA
    is rapidly working to capitalize on this technology.
        Our strategic vision is to make VA a leader in AI,
    providing faster services, higher quality care, and more cost-
    effective operations. We will aggressively deploy this new
    technology while remaining committed to strong controls that
    ensure security, privacy, and effectiveness of our technology
    systems.
        We have distilled this vision into five key priorities.
    First, we are aggressively expanding AI across our workforce.
    Second, we are reimagining high-impact workflows through AI and
    automation.
        Third we are prioritizing investment in data and
    infrastructure that supports those high potential use cases.
        Fourth, we are cultivating an AI-ready workforce. Finally,
    we are executing transparent and effective governance, an
    essential requirement to maintain veterans' trust. We are
    already bringing the strategy to life, making significant
    investments in AI-driven tools.
        In 2024, our AI inventory had 227 use cases in it, which
    was nearly 100 more than the previous year. We expect this
    growth to continue in 2025 as we prepare for our December
    update to that inventory. These investments are delivering
    tangible results. I am pleased to report that all VA employees
    now have access to secure generative AI tool to assist them
    with their work. In surveys, users of this tool are reporting
    that it is saving them over 2 hours per week.
        Additionally, over 2,000 VA staff and contract and software
    developers are using an AI software development copilot tool,
    enabling faster delivery of features that help veterans. AI is
    also revolutionizing clinical care. In fact, 82 percent of VA's
    AI use cases come from the Veterans Health Administration.
        VA's stratification tool for opioid risk mitigation uses
    machine learning to identify veterans at high risk of overdose
    and suicide, enabling healthcare teams to review and intervene
    effectively. Since 2017, the REACH VET program, as you
    mentioned, has used AI answer algorithms to identify over
    130,000 veterans at elevated risk, improving outpatient care
    and reducing suicide attempts.
        AI-assisted colonoscopy devices have increased adenoma
    detection rates by 21 percent, reducing late stage cancer
    incidents and mortality. Thanks to groundbreaking research by
    folks like Dr. Raffi Hagopian and Dr. Evan Carey, the VA is
    exploring how AI could help providers detect heart disease
    earlier by reviewing the millions of Computed Tomography (CT)
    scans that are not currently evaluated for cardiovascular
    disease risk at all.
        As we advance our AI deployments, protecting veterans' data
    remains paramount. All AI systems approved for use at VA must
    meet VA rigorous security and privacy standards before
    receiving an authority to operate. Additionally, consistent
    with Office of Management and Budget's (OMB) policy, we conduct
    a thorough agency-level review of each AI use case to ensure
    that it meets the government the standards.
        We will publish the results of this review in our annual AI
    inventory, positioning us as one of the most transparent
    healthcare systems in the country with regards to our use of
    artificial intelligence.
        Despite our progress, adopting AI tools does present
    challenges. As you mentioned, integrating new AI solutions with
    a complex system architecture and balancing innovation with
    stringent security compliance is crucial. Recruiting and
    retaining AI talent remains difficult. Scaling commercial AI
    tools incurs additional costs. This underscores the importance
    of full congressional funding for VA to continue this critical
    work.
        In conclusion, the Department of Veterans Affairs is
    committed to harnessing AI to improve the lives of veterans.
    Through strategic investments in AI tools and workforce
    capabilities, we strive to it deliver faster, higher quality,
    and more cost-effective services. Your continued support is
    vital for VA to lead in AI innovation and set a benchmark for
    responsible AI use in government.
        Thank you for the opportunity to discuss our strategy, and
    we look forward to your questions.

        [The Prepared Statement Of Charles Worthington Appears In
    The Appendix]

        Mr. Barrett. Thank you, Mr. Worthington.
        The written statement of Mr. Worthington will be entered
    into the hearing record.
        Mr. Ghatak, you are now recognized for 5 minutes to deliver
    your opening statement.

                        STATEMENT OF SID GHATAK

        Mr. Ghatak. My name is Sid Ghatak, and for almost three
    decades, I have designed and deployed artificial intelligence
    forecasting systems across finance, healthcare,
    pharmaceuticals, media, and government.
        I currently serve as the chief technology adviser for the
    National Artificial Intelligence Association, the premier
    organization representing 1,500 businesses in the advancement
    of AI. I am also the founder and chief executive officer of
    Increase Alpha, where we use artificial intelligence to predict
    stock prices, and we license these predictions to hedge funds.
        In the Federal Government, I served in the General Services
    Administration for 4 years where I was a Director of the Data
    and Analytics Center of Excellence. In that role, I coauthored
    the Federal AI maturity model 3 years before AI took the world
    by storm. I also contributed previous executive orders on the
    critical issues of data privacy and data security. At Increase
    Alpha, I increased a predict--architected a predictive AI model
    that generates off of once thought impossible, a deep learning
    system that is exceptionally accurate at predicting equity
    prices. Increase Alpha far exceeds multiple industry
    benchmarks, including accuracy, sharp ratio, and alpha
    generation. The solution itself is not based on large language
    models at all, but it is purpose built, designed for this
    specific need.
        I want to emphasize that this company and our solution is
    completely unrelated to the Department of Veterans Affairs, and
    it has no bearing on today's testimony. I mention it only as an
    example of how AI, when carefully designed with a clear
    purpose, can achieve exceptional effectiveness.
        Taken together, this diverse background, spanning academia
    and government and industry, has given me the rare opportunity
    to actually build AI systems that work well in the real world.
    I have spent my career outside the orthodox roles of academia,
    venture capital, and Big Tech, I am also not beholden to herd
    mentality. Instead, I bring an expert independent perspective,
    which is especially valuable now when much of the world is
    caught up in the art of the possible with AI when what is most
    urgently needed is a sober understanding of what is safe,
    practical, and ready to serve the public.
        Large Language Models (LLM) like ChatGPT, Claude, and
    Gemini are a powerful subset of AI, but they come with their
    own set of problems, specifically in healthcare where
    hallucinations and sycophancy on the part of ChatBots can lead
    susceptible users down psychological rabbit holes, which is why
    it is important to clarify that AI is bigger than just ChatGPT
    and its competitors. To use an analogy, the steam engine
    transformed society, fueling the Industrial Revolution. While
    steam power exists today, it gave way to other forms of power
    over time. Until steam engines were used to create the first
    railroads, no human had ever traveled faster than a horse. This
    new form of transportation opened the world's eyes to what is
    possible, just as ChatGPT has shown the world the art of a
    possible with artificial intelligence. Early train travel was
    dangerously unreliable. Accidents were frequent, derailments
    common, and thousands of lives were lost before rail systems
    matured into safe networks that we know today.
        The lesson is clear: Revolutionary technologies will evolve
    and improve over time when the private sector and the
    government work in collaboration. The same applies to
    artificial intelligence. As the committee gathers information
    on how to modernize technology at the VA, I would like to offer
    a few pieces of advice from my many decades on the front lines
    of building and implementing advanced analytical solutions.
        As I mentioned, the last several years, the world has been
    consumed with LLMs to the point where AI has become synonymous
    with it. However, that is not the case. Many other types of AI
    may have similarities to these models but function very
    differently, technologies that specialize in interpreting and
    understanding images, video, and audio, for example, or
    technologies that are better suited to working with numbers and
    symbols instead of words, a new technology that is yet to be
    invented.
        There is an old adage about, when you are a hammer,
    everything likes like a nail. The world has become so enamored
    with LLMs, and rightfully so, interacting with them can feel
    magical, giving you the sense that they are real people, but
    they are not. This may be why little to no investment is being
    made in these other areas. At Increase Alpha, we demonstrate
    clearly what can be done with other forms of artificial
    intelligence. I began building our models at the same time as
    the research underlying ChatGPT was published. I had also
    encountered the same compute cost energy and reliance on a
    video that we still see today. I took a different approach to
    conserve resources and focus on simplification, using
    predictive intelligence which led to leading AI models that use
    a minuscule amount of data compared to LLMs and which are small
    enough to run on a cell phone.
        What does all this mean for the VA and the well-being and
    care of veterans? I do not claim to know. No one really does. I
    want to leave you with a prediction: I believe that we truly
    are on the verge of a scale--of a revolution on the scale of
    the Industrial Revolution. If I could leave you with one idea
    today, it would be this: AI is much bigger than today's LLMs.
    It is these technologies, many of which have yet to be
    invented, that will enable the VA to execute on its mission.
    Thank you.

        [The Prepared Statement Of Sid Ghatak Appears In The
    Appendix]

        Mr. Barrett. Thank you, Mr. Ghatak.
        The written statement of Mr. Ghatak will be entered into
    the hearing record. I appreciate your remarks. I think, if we
    all use ChatGPT for cat memes, it will not be meeting its full
    potential and leaving a lot of things behind. Thank you.
        Dr. Ghassemi, you are now recognized for 5 minutes for your
    opening statement.

                     STATEMENT OF MOHAMMAD GHASSEMI

        Dr. Ghassemi. Chairman, Ranking Member, and members of the
    subcommittee. Thank you for the opportunity to speak today. I
    am a scientist and an entrepreneur focused on artificial
    intelligence but especially its applications to healthcare. The
    views I am going to share today are my own, but they are
    informed by roles I played as a professor at Michigan State
    University, where I direct a research laboratory on AI and its
    applications to health sciences.
        I am also going to bring a perspective as the founder of an
    AI consultancy Gamut Corporation, which has helped large
    pharmaceutical companies, insurance companies, as well as
    health systems, plan and execute their AI strategy.
        I want to be clear: I am not a veteran health specialist.
    My perspective is on how artificial intelligence can broadly
    advance care in ways directly relevant to the needs of
    patients, and this very critically includes our veterans.
        This subcommittee has identified in their invitation letter
    three priorities for AI health. These were transforming
    healthcare delivery, streamlining services, and improving
    outcomes. I am going to frame my remarks around three roles
    that AI can play to help with these three priorities. The three
    roles are automation, which is reducing low-value work through
    the use of machines; augmentation, which is having a machine
    assist a human in a task, so to strengthen clinical decision-
    making, as an example; and insights, which is allowing us to
    extract complex patterns from data, patterns far too complex
    for us to discern just with our human intuitions alone. Let us
    talk about three.
        First, AI can transform what happens during care itself.
    Clinicians today spend hours on paperwork, but AI scribes can
    generate notes automatically so they can focus more fully on
    patients. We have heard that from more than one person in the
    conversation today.
        In emergency rooms, decision tools powered by AI can help
    identify the sickest patient sooner and get them treated
    faster. Continuous monitoring assistance can pick up on the
    early signs of decline, like sepsis, long before they would be
    obvious to our human eyes. These tools make the encounter
    safer, timelier, and more patient-centered.
        Second, AI cannot only streamline what happens during care;
    it can streamline the plumbing of healthcare itself. Missed
    appointments waste scarce clinician time. Automated reminder
    systems, which do not have to use a large language model or a
    sophisticated tool like ChatGPT, can reduce these no-shows and
    save that time. Patients also too often fall between the cracks
    between primary care and specialist visits. AI can flag the
    missing referral information, track follow up, and prevent all
    these gaps. When imaging or labs reveal unexpected findings,
    like, God forbid, a lung nodule discovered by chance, AI
    tracking systems can ensure these findings are followed up on
    so that the treatable conditions do not get overlooked. This is
    how we reduce wasted effort and ensure smoother, more reliable
    care.
        In conclusion, artificial intelligence is not a silver
    bullet. I say this as a person who has been working on
    developing the methods for several years, but it can already
    help with the subcommittee's three priorities. It works best
    when it reduces low-value work, strengthens rather than
    replaces clinical judgment, and turns complex data into
    actionable insights.
        To succeed, we need disciplined pilots, clear metrics, and
    safeguards for safety, equity, and privacy. If deployed with
    care, AI can return time from paperwork to patients, ensure
    that critical findings are not missed, and support clinicians
    in their hardest decisions.
        I look forward to our conversation. I am grateful for the
    invitation to be here with you today.

        [The Prepared Statement Of Mohammad Ghassemi Appears In The
    Appendix]

        Mr. Barrett. Thank you, doc.
        The written statement of Dr. Ghassemi will be entered into
    the hearing record.
        Ms. Harris, you are now recognized for 5 minutes to deliver
    your opening statement on behalf of GAO.

                       STATEMENT OF CAROL HARRIS

        Mr. Harris. Chairman Barrett, Ranking Member Budzinski, and
    members of the subcommittee, thank you for inviting us to
    testify today on the use of artificial intelligence at VA.
    Develops in generative AI, which is a subset of AI, which can
    create text, images, video, and other content when prompted by
    a user, have revolutionized how the technology can be used in
    many industries, including healthcare and at VA and other
    Federal agencies.
        AI holds substantial promise for improving the operations
    of government agencies. However, it can increase risk for
    agencies and poses unique oversight challenges because the
    source of information used by AI is not always clear or
    accurate. Given the fast pace at which AI is evolving, the
    government must be proactive in understanding its complexities,
    risks, and societal consequences.
        It should also be noted that VA has experienced
    longstanding challenges in managing its IT projects and
    programs, raising questions about the efficiency and
    effectiveness of its operations and its ability to deliver
    intended capabilities.
        As requested, I will briefly summarize our prior work on
    the Department's AI use and challenges, as well as principles
    and key practices for Federal agencies, including VA, that are
    considering and implementing AI systems.
        In July 2025, we reported that VA's AI use cases increased
    from 40 in 2023 to 229 in 2024. For example, VA is a developing
    a generative AI use to automate various medical imaging
    processes. This use may enhance VA's ability to analyze medical
    images, integrate existing and new data workflows, and create
    summary diagnostic reports.
        In the health and medical sector, agencies have adopted
    generative AI to advance medical research and improve public
    outcomes, including at VA. It is also worth noting that, of the
    229 use cases, 64 percent were considered to be high-impact AI,
    meaning that their capabilities impact the rights and/or safety
    of individuals or entities. Looking at just VHA, that
    percentage increases to 72 percent.
        The Department also reported to us a number of challenges
    they face in using and managing generative AI. The full list is
    noted in my written statement. I will only highlight a few
    here.
        Challenge one, complying with existing Federal policies and
    guidance. VA officials shared that the existing Federal AI
    policy can present obstacles to the adoption of generative AI,
    including in the areas of cybersecurity, data privacy, and IT
    acquisitions.
        Challenge number two, having sufficient technical resources
    and budget. Gen AI can require infrastructure with significant
    computational and technical resources. VA noted challenges in
    obtaining or accessing the needed technical resources and also
    in having the funding necessary to establish those resources
    and support desired AI initiatives.
        The last challenge, hiring and developing an AI workforce.
    Among other things, the VA reported difficulties in
    establishing and providing ongoing education and technical
    skills development for their current workforce.
        VA officials told us they are working toward implementing
    the new AI requirements in OMB's April 2025 memorandum. Doing
    so will provide opportunities to develop and publicly release
    AI strategies for identifying and removing barriers and
    addressing the challenges I noted.
        Additionally, the GAO has identified a framework of key
    practices to help ensure accountability and responsible AI use
    in the design development, deployment, and continuous
    monitoring of AI systems.
        Our framework is organized around four complimentary
    principles that address governance, data, performance, and
    monitoring. Consideration of the key practices in this
    framework can help VA as it considers, collects, and implements
    AI systems.
        Last, I will mention that we have 26 open recommendations
    to VA concerning the management of its IT resources. If the
    Department implements these recommendations effectively, it
    will be better positioned to overcome its longstanding
    challenges in managing its IT resources and will improve its
    ability to address the rapidly changing AI landscape.
        That concludes my statement. I look forward to addressing
    your questions.

        [The Prepared Statement Of Carol Harris Appears In The
    Appendix]

        Mr. Barrett. Thank you, Ms. Harris.
        The written statement of Ms. Harris will be entered into
    the hearing record.
        Again, thank you to all of our witnesses.
        We will now proceed to questioning. I will recognize myself
    for 5 minutes to begin questioning.
        I am going to start with Mr. Worthington. The VA--I know we
    have got a lot of concerns obviously about data security, data
    privacy, what can be used, what can be modeled off of veteran
    information. The VA requires vendors to sign contracts directly
    stipulating that it will prevent secondary use of veteran data.
    Number one, can you kind of walk us through how that works? How
    are you making sure that companies actually follow that rule?
        Mr. Worthington. Thank you for the question, Chairman
    Barrett. I think it is extremely important that everyone
    understands that there is not a second set of rules for AI
    systems. In the VA, we have a very clear and stringent set of
    rules around both security and privacy for any technology
    system. Before we bring a system into production, we have to
    review that system for its compliance with those requirements
    and ensure that the partners that are working with us on those
    systems attest to and agree with those requirements. AI systems
    receive an authority to operate just like any other system
    would before we would put veteran data into the system.
        Mr. Barrett. Okay. I appreciate that. For example, though,
    I know the large language model, kind of most stereotypical use
    of AI, we are going to be looking at, you know, the millions of
    records that the VA has and then modeling patient outcomes from
    that and then looking kind of retrospectively to see where
    people are on that spectrum today, and say, ``Well, we know, if
    this condition led to 10 years later a worse condition over
    here, how can we stem that off earlier?'' If we allow an AI
    vendor to have access to that to cultivate that knowledge, is
    that something that could be then used as an outgrowth in
    another way for, like, another I guess research tool for other
    things? For example, if a person has a predisposition to kidney
    disease or diabetes or something like that, we can look
    retrospectively at their health record to show that they had
    certain indicators ahead of time, would not we want that to be
    to the benefit of all medicine and not just within the VA?
        Mr. Worthington. Yes. I think that, as you are mentioning,
    in the training phase of models, which VA does occasionally do,
    that, if we work with a vendor, we make sure that the
    agreements say that any protected health information can only
    be used for that specific purpose that we have contracted with.
    Often, that is taking place in environments that VA already
    runs and controls.
        Now when we are talking about using a large language model,
    those are provided typically via one of the big cloud service
    providers, and those environments are set aside in a VA
    boundary that basically the vendor has to attest that they
    already meet VA security requirements. When we are sending
    information to a large language model to get feedback back from
    that model, we are using a version of that model that has been
    made secure to meet government standards.
        Mr. Barrett. Okay. I will fully confess that I am not an
    expert on this. Would a large language model allow a
    practitioner to say, ``I have a veteran presenting with these
    conditions; what are the risk factors that I ought to look for
    to, maybe run tests that would not ordinarily be otherwise top
    of mind?''
        Mr. Worthington. There could be a variety of AI approaches
    for a use case like that. Dr. Carey may just quickly provide a
    couple of examples of those sorts of decisions support type use
    cases.
        Dr. Carey. Thank you. It is a fantastic question. I think
    there are two versions of that. As you note, there are tools
    where providers can get general advice, and they might
    specifically articulate the needs of the veteran and for the
    conditions that they are looking for, to point out to sort of
    follow the different procedures that are recommended and
    identify the guidelines. Those tools are available within the
    VA.
        Mr. Barrett. Okay. After the passage of the The Sergeant
    First Class Heath Robinson Honoring our Promise to Address
    Comprehensive Toxics (PACT) Act, you know, we have this burn
    pit registry and everything, and they are supposed to track
    veterans and conditions that arose from that. Obviously, the
    specific information about a particular veteran we want to have
    protected and not revealed. If there are outcomes of that that
    could be useful to, you know, human medicine in total, is there
    a way for that to be revealed?
        Mr. Worthington. Yes, thank you for the question. VA does
    have, as you know, a very large amount of health data. We have
    a robust----
        Mr. Barrett. More than anybody in the world, I think.
        Mr. Worthington. That is right. We have a robust tradition
    of research to advance not just VA healthcare but healthcare
    overall. We are seeing an increasing interest in using that
    data for AI-driven research papers, like the one that Dr. Carey
    recently wrote.
        Mr. Barrett. Okay, and that is the--like the benefit but
    also the concern is we obviously have a large repository of
    medical data. If that is being used or to the benefit of a
    curator of artificial intelligence, should the VA be, you know,
    should that be brought into account for the cost of services
    and other things like that? What I do not want is for a
    provider to come in and leach that information out solely for
    their benefit while not providing a benefit to the VA and to
    the veterans as well.
        Mr. Worthington. We agree.
        Mr. Barrett. Thank you.
        Ranking Member Budzinski.
        Ms. Budzinski. Thank you, Mr. Chairman.
        Dr. Carey and Mr. Worthington, thank you so much for both
    being here.
        I understand that several of VA's AI use cases, like the
    ambient dictation pilot, intend to use an opt-in practice for
    consent. For systems that are perhaps less directly veteran-
    facing, like the use of AI in benefits determination or medical
    assessments, how is the Department educating veterans on these
    use cases to ensure for their awareness?
        Mr. Worthington. At a very high level--and thank you for
    the question. We are using our AI use case inventory as the way
    to catalogue all of the uses of AI and make sure that that is
    publicly available. When there is not, as you mentioned, like a
    one-to-one interaction that provides the opportunity to explain
    directly what is happening, as there is in many healthcare
    settings, what we are relying on is our publishing of the
    overall AI strategy and use case to explain how the Department
    is using AI in various products and services.
        Ms. Budzinski. Okay. Other than that general awareness--for
    veterans, is there any way to kind of draw their attention to
    this so that they know that, you know, what their situation
    might be using to inform an AI model?
        Mr. Worthington. We are always listening for veterans'
    feedback through a variety of mechanisms and reacting to that.
    That is true of AI situations and non-AI situation. We
    certainly want to monitor this for AI in particular, because I
    think maintaining veterans' trust in VA as we introduce these
    new technologies is going to be critical.
        Ms. Budzinski. Okay. Then, Mr. Worthington, I am glad that
    you and your teams are committed to the transparency in AI use
    cases at the Department. That is commendable. However, there
    have been reports that certain employees had access to certain
    data sets and systems within VA's enclave which may have been
    used for AI related operations. I have some specific employees
    I want to mention by name, and then I have some questions for
    you. I am going to ask about these employees: Justin Fulcher,
    Sahil Lavingia, Christopher Roussos, Payton Rehling, Cary
    Volpert, or Jon Koval. I am just looking for, like, a yes or no
    to these questions. Did you ever work with any of those
    individuals?
        Mr. Worthington. Yes, I have come across several of them.
        Ms. Budzinski. Okay. Are or were these individuals
    affiliated with the Department of Government Efficiency (DOGE)?
        Mr. Worthington. I am not exactly clear on the
    relationship. I believe they are VA employees. At points, they
    were introduced as also being part of the DOGE movement.
        Ms. Budzinski. Okay. Did any of these employees access data
    sets that included VA patient medical records or other
    personally identifiable information?
        Mr. Worthington. I am not aware.
        Ms. Budzinski. Okay. Were you or anyone you know ever asked
    to duplicate data sets by these employees?
        Mr. Worthington. No, I was not.
        Ms. Budzinski. Okay. Can you commit to me that no veteran's
    data was removed from the Department of Veterans Affairs?
        Mr. Worthington. As far as I understand, all the VA
    employees follow all the VA IT security processes and
    procedures and that was a key priority for all of us and always
    is a key priority.
        Ms. Budzinski. Okay, Okay. Mr. Worthington, almost 2 weeks
    ago, the Acting Director of the U.S. Digital Service noted that
    the Federal Government needs more tech employees to--and to
    hire and empower great talent. Do you believe that VA shares
    that sentiment?
        Mr. Worthington. Yes, I do. I think having technologists in
    government is critically important, as is having great
    researchers and doctors.
        Ms. Budzinski. Okay. Secretary Collins has often noted the
    importance of VA employees in direct care roles, disregarding
    the importance of what he might call support employees in the
    provision of this work. Do you believe that this type of
    rhetoric has helped the Department to recruit and retain tech
    talent?
        Mr. Worthington. I think the good thing about working at
    the VA is our mission is so clear. The mission of serving
    veterans is the most important one that I have worked on in my
    tech career. I think there are many technologists across the
    country that are willing to sign up for that mission. I love
    trying to recruit those people on my team.
        Ms. Budzinski. Ms. Harris, real quick on a follow up, GAO's
    Artificial Intelligence Accountability Framework notes the
    workforce is a key component to ensuring effective AI
    application. How does a highly skilled technical workforce
    ensure adequate scalability of AI applications and protection
    of veteran data?
        Mr. Harris. Well, while there is great excitement around AI
    because of the potential to improve operations, there is also
    significant concerns, the ones that I articulated earlier about
    cybersecurity, intellectual property, as well built-in bias in
    the AI system, as well as environmental and other concerns. We
    want to make sure that we have a workforce that understands
    both the potential of these systems but also understands the
    risks in the AI well. Having those two are vital.
        Ms. Budzinski. Okay. Thank you.
        I yield back.
        Mr. Barrett. Thank you.
        Mr. Luttrell.
        Mr. Luttrell. Mr. Chairman.
        Mr. Ghassemi, you laid out a well-articulated plan of
    attack on how the VA could tackle this healthcare, artificial
    intelligence kind of combining of forces. The problem is you
    have--it sounds like you never worked with the U.S. Government
    because that is what kills this effort is the U.S. Government.
        Ms. Harris, your opening statement was very well-
    articulated, and you hit every single point precisely. The
    problem is we have such an issue with the VA because it is a
    big machine, and we are trying to compound--we are trying to
    bring in artificial intelligence to streamline the process. You
    have 172 different VA facilities, plus satellite campuses, and
    that is 172 different silos. They do not work together. They do
    not communicate very well with each other. We have spent almost
    $16 billion trying to push electronic healthcare records across
    multiple facilities. Now we are going to try to tackle
    artificial intelligence as well. In 2024, we had 229 AI
    actions. Correct, Mr. Worthington?
        Mr. Worthington. Yes, approximately.
        Mr. Luttrell. What site did that come from, because I would
    dare say that that did not come from all or every single VA
    installation. That sounds like to me that that is collected
    from, like, a few. Is that correct?
        Mr. Worthington. We did attempt to have a pretty
    comprehensive review process to gather all of the uses of AI
    across the country. We----
        Mr. Luttrell. I did not get anything out of that. That was
    almost a yes-or-no question, but go ahead again.
        Mr. Worthington. Yes, I believe that AI is being used at
    facilities across the country. This inventory covers those
    uses.
        Mr. Luttrell. The conversations I have with multiple sites
    is they do not have artificial intelligence capabilities
    because their sites are not ready or they do not have the
    infrastructure in place to do that, because we keep compounding
    software on top of software. Some sites cannot function at all
    with the new software they are trying to implement. That is a
    pretty fair statement, correct?
        Mr. Worthington. I would agree that having standardized
    systems is a challenge at the VA. There is a bit of a
    difference in different facilities. Although I do think many of
    them are starting to use AI-assisted medical devices, for
    example, and a number of those are covered in this inventory.
        Mr. Luttrell. How do we fix this problem? Again, I am going
    to ask you, sir, because I usually ask everyone who sits in
    front of me from the VA: How would we fix this problem? Mr.
    Ghatak and Mr. Ghassemi have probably thought about this quite
    a bit before they showed up in front of us, but again they have
    not--actually, I do not know this for certain--I may be
    throwing this at you, and course correct me if you would like--
    but I do not think they have had to deal with the U.S.
    Government and also the VA. Now how long have you been in this
    position, sir?
        Mr. Worthington. I have been at the VA nearly 10 years and
    this position for about 2 years as chief AI officer.
        Mr. Luttrell. Okay. What comes first, the communication
    between the sites and the ability to ask that information
    questions, which we do not do that or we do not have the
    ability to do that--do we run the implementation of artificial
    intelligence in parallel with that, or do we have to do one
    before the other?
        Mr. Worthington. In my personal opinion, we cannot wait,
    because AI is here, whether we are ready or not. Increasingly,
    every solution we buy from our partners in the private sector
    is going to have it embedded inside of it. I think our
    challenge is we need to come up with very good standard
    templates that every site can use and allow those standard
    tools to be deployed, things like the VA GPT school that I
    mentioned, which is now available to every VA employee in a
    standard way.
        Mr. Luttrell. Since the Department of Veterans Affairs
    houses the most important data set on the planet arguably, and
    everyone wants to touch it, including Dr. Ghassemi at Michigan
    State--I would have to guess, especially when you were at
    Massachusetts Institute of Technology (MIT) in Cambridge I am
    sure. Pretty impressive resume, sir. Everybody is trying to
    touch it. Everybody wants to be a part of it, and you have to
    deal with every single subject-matter expert that walks through
    your door that says, ``I am the best.'' I can assure you every
    one of those corporations and companies walks into our office
    as well. Question is, who is it? Who do you vet, and who is
    going to touch it, because it cannot be everybody? We do not
    have it--in my personal opinion, that I am not aware of, we do
    not have an enclave that can house all of that information
    where everybody can get in there and not steal it.
    Implementation of artificial intelligence, which we do not have
    the ability to regulate, so the question is who will do that,
    or do you have the AI system itself regulate itself?
        Mr. Worthington. I think it is a great observation and
    concern; it is one we share. The reason why we are putting
    every AI use case through that review process is to ensure
    that, if it is being used with real veteran data, that it meets
    VA's stringent security requirements.
        Mr. Luttrell. Thank you, Mr. Chairman. Thank you, sir.
        I yield back.
        Mr. Barrett. I thank you.
        Ms. Cherfilus-McCormick.
        Ms. Cherfilus-McCormick. Thank you so much, thank you so
    much.
        I wanted to kind of piggyback off of some of Representative
    Luttrell's questions. You mentioned standardization, and we
    know now, from doing this for years, that standardization in
    the VA has not been our strong suit. Are there any things that
    you have learned from our lack of standardization for all of
    our electronic medical records? We have been consistently
    having an issue there with standardization. I have two
    questions for you first. Are you confident that you can
    actually have a standardization mechanism that will be able to
    have a smooth transition implementation?
        Mr. Worthington. Thank you for the question, and it is a
    critical topic for us. I do think that the investments this
    committee has helped make over the past years has helped with
    that. We do have, for example, in the space of decision
    support, we have an investment that allows AI-assisted decision
    support tools to be purchased or built and then deployed to
    every Veterans Health Information Systems and Technology
    Architecture (VistA) site and also to every----
        Ms. Cherfilus-McCormick. I guess my question really is,
    like I said, we have been trying to be successful here, and it
    has not been. How confident are you now? What are the missing
    links for standardization when it comes to AI, because AI has
    some complexities that I think we can all acknowledge,
    especially when it comes to biases? If we are going to
    implement AI into our system, we want to make sure that we have
    precise implementation, and we are also taking into
    consideration responsible implementation of AI, which actually
    addresses the biases immediately, that deals with security
    immediately. I was going to go into those questions first, but
    I said, ``I cannot even go there if we do not deal with
    standardization.'' What have we learned? How confident are you,
    or should we really be taking some time to step back and look
    at standardization again but through a magnifying glass to make
    sure we get it right?
        Mr. Worthington. I do feel confident that we are
    approaching this in an enterprise approach. That is why
    partnerships with the VHA and our colleagues, like Dr. Carey,
    is so critical. AI is both a new area--it is one we need to be
    able to experiment in before we commit to that enterprise
    solution. Then, once we commit, we do not want to have, you
    know, every medical center buying its own version of the same
    product. We have got a pretty careful balance of that
    innovation. We are doing structured pilots to help us decide
    what to purchase and what to deploy to the enterprise.
        Ms. Cherfilus-McCormick. I wanted to talk more about the
    implementation development because we know that most of the
    biases will be during the development phase and also the
    implementation phase. What are you doing specifically to make
    sure that these biases are not being inherently put into the
    system, to make sure that all of our veterans actually have
    access to equitable care?
        Mr. Worthington. That is a great question, and it is a
    concern that is of critical importance for us as we adopt AI.
    The Office of Management and Budget in their policy has
    determined, defined high-impact use cases. Those would be
    things involved in healthcare benefits. They have provided us a
    set of requirements that any AI needs to meet before they are
    used. Some of the highlights of those are pre-deployment
    testing to make sure the model performs well across different
    demographic groups, but not just pre-deployment testing but
    also ongoing monitoring so that we can make sure that the
    models perform over time.
        Ms. Cherfilus-McCormick. Could you tell me how you are
    doing that? We have been reading--I have been loving this AI
    conversation I have been looking at through all spectrums. One
    of the articles that I am going to actually ask to put into the
    record, it talks about the clinical decision-making the
    implementations. I also want to hear from Ms. Harris about, are
    we matching the need right now to identify bias?
        Mr. Worthington. I do believe that we, through the AI use
    case control process and the governance we put in place with
    our partners in VHA, that we do have a commitment from all the
    use case owners to meet those standards in the OMB
    requirements.
        Ms. Cherfilus-McCormick. Ms. Harris, what would you like to
    see when it comes to actually being vigilant on making sure
    that we are not utilizing a system that has inherent biases in
    it?
        Mr. Harris. For sure. One thing to note--even Mr.
    Worthington talked about these high-impact systems--VHA has 72
    percent of their AI use cases as being high impact, so meaning
    that they affect people and entities and their rights. That is
    quite a number, a high number of systems that have that
    implication. Yes, you have to go through additional hoops, as
    he had mentioned, with pre-deployment and during monitoring to
    make sure that, you know, rights are not compromised. The VA
    has told us that there is a need for more privacy officers to
    handle increased data security demands. We would like to see
    more of those positions being filled to ensure that privacy is
    really taken care of as it relates to these high-impact uses
    case.
        Ms. Cherfilus-McCormick. I have a few seconds left, but I
    did want to ask Dr. Ghassemi, are there any cases that you have
    seen in public usage or private usage where they have done an
    excellent job in actually removing the biases, identifying them
    immediately?
        Dr. Ghassemi. There is a really active domain of
    researchers who are trying to solve exactly that problem. A lot
    of the studies are happening with, for example, the Medical
    Information Mart for Intensive Care (MIMIC) data base, which is
    based out of the Boston area, something that I actually
    contributed to.
        To summarize, I think the broader domain of that research
    activity, in a few words, is it is possible to do it, but it
    requires a thoughtful approach, and each data set is different.
    What you have in the VA and the bias in that will be different
    than if you are doing it in the context of a data set in Boston
    with somewhere else.
        Ms. Cherfilus-McCormick. Thank you. I yield back. Thank you
    for your time.
        Mr. Barrett. Thank you.
        I will recognize myself for 5 minutes again.
        Dr. Ghassemi, I wanted to come back to you, and you have
    listened to some of the back and forth testimony and some of
    the responses, both from the VA and from members here. You are
    outside of the VA. You have the benefit of being removed from
    some of this internal stuff. I am curious, you know, kind of
    what your thoughts are to me, and to Mr. Luttrell's point is we
    are trying to upgrade this legacy health record system on a I
    guess parallel track, to use the term you used. We are trying
    to modernize some of the easy lift items that can be done
    through assisted technology or augmented, I think somebody said
    in their testimony as well. Do you think that is achievable,
    number one? You know, how do you think that the VA can do this
    responsibly to make sure that it is done in the appropriate
    way?
        Dr. Ghassemi. The short answer is I think it is achievable.
    How can it be done responsibly? It has to start first and
    foremost with unification of the data. I heard earlier
    conversations that----
        Mr. Barrett. In unification of data, are you talking about
    having a singular system, or are you talking about the data
    itself not being fragmented across all these different VA
    facilities?
        Dr. Ghassemi. What I mean is that you need a singular way
    to represent the data so that an AI system that operates in one
    system can move and operate in another. Now, actually the good
    news is that artificial intelligence can be used to help with
    that unification process itself. I will speak about some of my
    external experiences here and say why I think there is room to
    be helpful. It is a common problem in industry for corporations
    to deal with. They have a large data base of customers, or
    health systems have a large data base of patients, and they
    want to enrich that with some data from outside of their
    ecosystem. That is a common problem. There is reconciliation of
    two complex data sets where column names in these data sets do
    not match, representations of values inside these data sets do
    not match. There is so many things that are misaligned here.
    The same, instead of thinking of AI's role as coming in after
    you gave done a very heavy duty and costly and inglorious task
    of aligning that data, you can use the AI tools to perform
    alignment of that data, right, to ask how you do the
    combination of the information, the debiasing considerations
    that were brought up earlier, and so on.
        Mr. Barrett. Thank you, I appreciate that. How do you think
    balancing, you know, the access to this and the benefit that
    comes from it with keeping the paramount interest of, you know,
    veterans' consent and privacy and all of those things that we
    cannot miss the mark on as well? I would be interested in your
    thoughts on that.
        Dr. Ghassemi. Yes, I think disclosure is really important
    transparency. You know, when we go to a supermarket and we turn
    around an item that is on the shelf? On the back is disclosed
    to us through nutrition label what are the contents inside of
    the food that we purchase. In a similar way, if you think of
    care that we receive as an item, then you need a similar way to
    inspect what components, which parts of the ingredients in that
    care came from which sources. Did they come from a model that
    Oracle trained on their Cerner ecosystem? Did they come from an
    academic paper? Did they come from a clinician's judgment? The
    traceability of that decision and making it transparent back to
    the end consumer of the care, which is the veteran, that is
    really important because they have a right to know how care is
    being derived prior to consenting to receive it. I think that
    transparency sits at the beating heart of doing this correctly.
    The reason there is trepidation, as far as I understand it,
    behind the use of AI--not just in healthcare by the way, but in
    a large number of industries, is because the transparency is an
    issue, right? It could tell you--hallucinations--I think maybe
    some of you have heard of this concept. If you have not, I will
    quickly define it--is when a model basically confidently tells
    you the wrong answer. There are ways to overcome this. They
    require some expertise, but it is solvable.
        Mr. Barrett. Thank you. I appreciate it. I am out of time.
        Ranking Member Budzinski, I will recognize for you 5
    minutes.
        Ms. Budzinski. Thank you very much. September is Suicide
    Prevention Month, and our full committee has not had a hearing
    for many years on suicide prevention, which I think is
    something that is a very big missed opportunity and something I
    am hoping we can be getting to.
        I can use this opportunity at this subcommittee hearing to
    ask the VA some questions around suicide prevention and then
    the connection with AI and how AI might be a useful tool
    suicide in prevention, like the REACH VET algorithm model, in
    particular. My question is for actually Dr. Carey. Can you
    speak to how VA is planning to use its AI inventory to build on
    this success?
        Dr. Carey. Absolutely. Thank you so much for the question.
    As you know, it is incredibly important that we take care of
    our veterans, especially in this context of mental health
    needs. We have been operating the REACH VET model for a number
    of years, as Mr. Worthington noted, since 2017 successfully. We
    have updated that model recently to ensure it has ongoing high
    performance of identifying identification of veterans at the
    highest risk core tiles. Then we implement that model as part
    of a multipronged strategy to ensure veterans get the care they
    need. Their receipt of the care they need does not depend only
    on identification of an AI tool or being flagged as being at
    high risk. It is just one of many strategies we use to ensure
    that veterans are regularly screened, and, as you noted, in the
    opening statement, if anybody falls through the cracks, that
    they have an opportunity to still receive the care they need.
        Ms. Budzinski. One of my concerns is just we do not want to
    prevent human involvement from being a part of suicide
    prevention. We can use AI as a tool. How does the VA look at-
    you know, working to ensure that human involvement is not
    eliminated as a part of the critical nature of the care that we
    want to be able to provide to a veteran with suicide prevention
    efforts?
        Dr. Carey. Thank you. That is a fantastic question, and we
    completely agree. I want to make it absolutely clear that VA
    clinicians deliver care to veterans. VA clinicians are in
    control of the care that veterans receive. While we do use AI
    tools to surface risks and ensure that all veterans are flagged
    to get the care they need, what happens next is that a human at
    the VA reaches out to that veteran, where it first reviews the
    information and decides if outreach is necessary.
        Ms. Budzinski. Okay. Could you commit for me that the VA
    will never use AI, including chatbots, as a substitute for
    frontline staff responders for mental health crisis
    intervention?
        Dr. Carey. We do not currently have any plans that I am
    aware of to use AI as a treatment device instead of providers.
    I personally have been a part of many conversations where we
    ensure that continues to be the case.
        Ms. Budzinski. Okay. Thank you.
        Ms. Harris, could I ask, what risks are posed by using AI
    tools for use cases other than their intended purpose, like the
    use of chatbots that were developed for programs like Veterans
    Readiness and Employment (VR&E) or home loans and crisis
    intervention support?
        Ms. Harris. Well, I think that there would be significant
    risks in a tool that is not being performed as intended.
        For example, if you are using an AI chatbot for one
    program, but, you know, obviously if you use that same bot for
    another program, it is going to produce poor results. That is
    because the data that was used to teach that tool would not be
    relevant to the expected role for that other program. We would
    certainly think that there is significant risks in dealing with
    what you have asked.
        Ms. Budzinski. Okay. Then I guess the VA's Office of
    Inspector General reported in April that Veterans Benefits
    Administrations (VBA) automated decision support tool was
    ineffective in helping claims processes assign the correct
    effective date for PACT Act claims. This resulted in at least
    $7 billion in improper payments. I worry that VHA's rushed to
    expand automation will lead to similar errors that could put
    patients at risk.
        Shifting gears, Mr. Worthington, how do you plan to measure
    accuracy of implemented and piloted AI tools?
        Mr. Worthington. That is a great question. I think by
    having all of the use cases documented, along with the owner of
    each AI use case, we will have the consistency plans available
    to us so that then our colleagues and VHA can be regularly
    following up to see what they found. We agree that continuous
    monitoring of AI in production is very important.
        I do think our healthcare system is particularly well
    designed to monitor for those sorts of things because that is
    part of what they do in a non-AI context as well.
        Ms. Budzinski. Okay. Just a quick follow up. At the hearing
    on this topic, Mr. Worthington, last year, you mentioned that
    the key to understanding how any particular AI may introduce
    biases is to understand the data that it was trained on and the
    outputs it provides.
        Considering the efforts of this administration to limit
    what kind of data may be available in research data sets or in
    a veteran's medical file, do you believe that this will impact
    the efficacy of VA's AI tools?
        Mr. Worthington. I would have to get into the specifics of
    any given case. I think, at a high level, it is very important
    to understand what data went into the training and do pre-
    deployment testing before we use something in production.
        Ms. Budzinski. Okay.
        I yield back.
        Mr. Barrett. Thank you. I will now recognize Mr. Luttrell.
        Mr. Luttrell. Thank you, Mr. Chairman.
        Dr. Ghassemi, I am fascinated with your previous statement.
    Clean data, dirty data, retrospective, prospective data, the
    transfer of information is very challenging. I am not going to
    say impossible. I will never say that.
        Currently, the VA does not house all of veterans' data. It
    sits in the different silos of the different hospitals. I think
    the death records lives in one spot, but everyone else is
    assimilated, right? Correct?
        Mr. Worthington. There is definitely siloed systems,
    although our health data is pretty consolidated.
        Mr. Luttrell. Consolidated. It make senses to me--and I do
    not know the price tag on this, if this is even possible, that
    if all the data lived in one enclave, the entire veterans space
    lived under just say the VA data center--which I do not even
    know what that would look like--but then the VA could control
    access to anybody, including all the sites, plus every single
    university and research student, whoever it wants to touch it,
    and they could prevent the ability for data theft. Is that a
    fair statement? Anybody?
        Mr. Worthington. I do think that consolidating data into
    secure platforms can be a good enabler of this sort of
    technology for sure.
        Mr. Luttrell. Are we even having that discussion inside the
    VA? You can say no.
        Mr. Worthington. Yes, we were actively working and, in
    fact, have done a number of data consolidations to make that
    possible.
        Mr. Luttrell. I have been here for about 3 years now, and
    the word ``activity working,'' it does not really resonate in
    this place.
        Are we really wanting to do this, or is this just something
    that is just something you are throwing at me?
        Mr. Worthington. No, I think like an example, like the
    REACH Vet model that we just tried is a model that was created
    based on that consolidated data set that draws on data from all
    the different medical centers as well as other data into one
    central data warehouse.
        Mr. Luttrell. Everybody can touch it. If somebody in
    Conroe, Texas, a VA facility that I have says, ``Hey, look I
    have a veteran here that has this,'' they can reach out to that
    data center, populate from tens of trillions of data points,
    and send back, ``Hey, most likely this is what we are looking
    at''?
        Mr. Worthington. Well, when you are using it--it gets
    complicated quickly, as you know.
        Mr. Luttrell. I know.
        Mr. Worthington. Different use cases have different degrees
    of connectedness. In terms of building places where we can
    create those models that we just went through like REACH VET,
    we do already have investments that help with that.
        Mr. Luttrell. Okay. If we do have the willingness to do
    this, somebody is going to have to have the software in place
    to do it. Mr. Ghatak, I am not going to let you out of here
    without saying something. Okay.
        Who can handle something like this? Company-wise, industry,
    whoever? Do not say Michigan State because you are sitting in
    the room with me.
        Mr. Ghatak. No, sir. I would say University of Michigan
    where I went to school, they could probably take----
        Mr. Luttrell. They are pretty good, too? Okay. Yes.
        Mr. Ghatak. Sir, I spent 4 years in the Federal Government,
    I have worked in the General Services Administration under
    Technology Transformation Services (TTS), and I had the
    opportunity to work with a lot of different agencies in that
    capacity. What I saw there was what I had seen throughout my
    commercial career, which is, as I put in my written statement,
    organizations have way more data than they realize. That data
    exists in more locations than they are aware of. That data
    means different things in different places at the fundamental
    root level in terms of where the data exist. The number one
    reason that projects fail--if it is an AI project or if it is
    any other technology project, it is because of the data. If the
    data is not there, then no matter what position, what solution
    you have, it will never really work. It is sort of like what we
    call lipstick on a pig, in other words. You have to solve that
    problem.
        Now, who solves that problem? That is an enterprise wide
    problem. That is an enterprise wide acknowledgment that the
    problem exists, and then an enterprise wide effort to make the
    investment in solving that problem from a----
        Mr. Luttrell. Multiple agencies are going to have to come
    in on top of this.
        Mr. Ghatak. I would say multiple departments within an
    agency would report up through a business leader, a chief
    officer, reporting up at the highest level to make that
    investment and to solve that problem at the fundamental level.
    Because if it is not solved fundamentally, then the underlying
    structure of any solution will not work.
        Mr. Luttrell. I am going to make the assumption, which I
    probably should not. This is what is going to have to happen.
    Yes?
        Mr. Worthington. I think we need to find ways to get the
    exact right piece of data from everything that VA and U.S.
    Department of Defense (DOD) have access to the person that
    needs it at the right time. I actually think that search-and-
    summarization capability is actually one of the things that we
    are excited about AI may be being able to help with.
        Mr. Luttrell. This is what AI will do for us.
        Mr. Worthington. I think it could help with those sorts of
    things to sift through all----
        Mr. Luttrell. I do not think the human brain could process
    that many data sets.
        Mr. Worthington. That is right. This is one of the areas we
    are actively investing in.
        Mr. Luttrell. I should not say that. The human brain could
    absolutely do anything; the human being cannot.
        Mr. Worthington. I think it gives an opportunity to empower
    people to act on more information than they would be able to do
    manually.
        Mr. Luttrell. That is something that--the kind of
    downstream I would like to--you know, I would like to see the--
    how we are laying this out. At the end of the day, as
    appropriators, in Congress, we are going to have to put a
    dollar sign on that. Since Electronic Health Record (EHR) is
    really giving us a great time, you kind of see where I am going
    with this?
        Thank you, Mr. Chairman. I yield back.
        Mr. Barrett. Yes. Thank you, Mr. Luttrell.
        I will recognize myself for 5 minutes.
        Mr. Ghatak, you mentioned in your testimony, kind of
    compared AI to the early days of the railroad, right? You know,
    this was a great advancement, but it was fraught with all these
    problems and challenges, and, you know, over time was
    perfected--and I guess never truly perfected, but certainly
    perfected to the degree that we can reasonably get to.
        I think when it comes to artificial intelligence, there is
    a greater risk than the occupants of a train rolling down a
    railroad track. This could have catastrophic outcome if left,
    you know, unguarded or breach of information or, you know, who
    knows what. It could be truly problematic.
        What are the guardrails that you think are appropriate and
    necessary right now to make sure that that does not happen with
    AI? Like how are we going to look over the horizon of what
    could happen and prevent it from happening on the front end?
        Mr. Ghatak. Thank you. It is a great question. I think it
    is a very--it is sort of a fundamental question in terms of AI
    and what it is and what it is not. As I said in my statement,
    when you interact with AI tools today, it feels like you are
    talking to a human being, but it is not a human being. It has
    no moral conscience. It does not really understand the words
    that is actually being given to it or the words that it is
    producing.
        There are a number of ways to really address this issue.
    One of those is really understanding the difference between
    correlation and causation without getting into great
    statistical detail.
        There is nearly a perfect correlation--as I put in my
    testimony--in terms of the number of Google searches for the
    word ``Nintendo'' and the number of librarians in the State of
    Michigan. Most statistical models will rely on this relation--
    since I am using correlation--to identify patterns and then
    reproduce those patterns in its output. What is really needed
    is an emphasis on causation, understanding the inputs that a
    model uses, how those inputs relate to each other, and how
    those relate to the outputs.
        There is very little effort being placed on that type of
    technology and that type of investment because the dollars are
    already chasing correlation. Correlation is a lot easier to do
    than causation. That is where a lot of the investment goes.
        I would say one of fundamental areas is--and I do not know
    if it can be mandated, but I would think--I would hope that the
    scientific and research community would realize that is the
    power of AI, is to unlock the true potential of it, is to
    really mimic how a human mind works, which is it sees something
    and reacts to it, and then produces something else. To mimic
    that with other technology would be great.
        The other thing that I did want to say is going back to the
    data itself. A model is trained. I think the question was
    around bias, right? The data that the model is given, if it is
    not inherently debiased, if a lot of thought is not given to
    the data itself that the model receives, then the output will
    be inherently biased. It could be biased because of the way it
    is engineered. It could be biased because of the data that it
    is given. Because these models are so complex and so little
    work has been done to understand how they work, we will never
    know if it is the model that is biased or the data that was
    biased.
        Again, a principle, a development principle, a research
    principle, a standardization that is adopted by industry to
    address all of those would be very helpful.
        Mr. Barrett. Yes, thank you. That correlation-causation
    thing is really important. I would bet or guess that a lot of
    information at the beginning is correlation information. Over
    time, maybe it can be perfected or improved into the causative
    and non-causative, you know, parts of that. At the beginning,
    it is ``if this, then that'' correlation. We may not know why
    or how, but these things, especially when you are dealing with
    medical information over a long period of time, and, you know,
    if enough people come in with a correlating condition, enough
    times we begin to believe it is causative for a risk factor for
    something else.
        I guess how do we--like how do we make good decisions based
    on that? You know, because we may not even understand the
    causative nature of it, but if it is enough correlation data
    there, maybe it does tell us something.
        Mr. Ghatak. Absolutely. I think correlation has a purpose
    in terms of identifying patterns or identifying things that are
    outside of the norm, absolutely. It is a wonderful tool, and it
    is a critical tool. My position would be that it just cannot be
    used in a vacuum. That coupled with understanding causation and
    investing more in those types of tools to help understand the
    true relations between these things and why one is causing the
    other.
        As I mentioned, there is no obvious relationship between
    Google searches and the number of librarians. The problem is
    correlation models do not know that.
        Mr. Barrett. Right.
        Mr. Ghatak. They just Google that number and run with it.
    There are a lot of crazy examples that I can give, but that is
    a good one and relevant. That emphasis on causation I think is
    really one that has not been invested in as much as it should
    be. It is something that we found that is really helpful and
    powerful that helps us understand our models and why they came
    that way. Also, when they fail, we understand why they fail. It
    is likely because something broke in that relationship or did
    not work in that relationship.
        Mr. Barrett. Thank you. I am out of time.
        I am going to yield to Ranking Member Budzinski for 5
    minutes.
        Ms. Budzinski. Thank you. Thank you, Mr. Chairman.
        I wanted to ask Ms. Harris some follow-up questions. Just
    as ranking member, I have spent now a lot of time asking the VA
    how it plans to juggle all of these different numerous
    modernization efforts the Department is pursuing, like
    Electronic Health Record Modernization (EHRM), of course,
    supply chain, HR modernization, and now AI.
        I was wondering if you could speak to the types of
    resources that the VA will need to consider having at its
    disposal as it deploys these systems.
        Ms. Harris. Yes, thank you for the question. I mean, first
    and foremost, I think it is hugely problematic that VA does not
    have a permanent CIO in place. I know you mentioned it in your
    opening statement. That is because, under his or her
    leadership, that is where these, you know, various IT
    modernizations get prioritized, you know. Plus our work has
    shown that, you know, when you have that steady leadership
    over, you know, 3-to 4-year time period, that is essential for
    any successful major IT initiative, including all the AI
    initiatives, Zero Trust, EHRM, all those things.
        The second point, OIT is obviously going through a major
    restructuring right now. They have requested almost $300
    million less in Fiscal Year 2026 than the previous year. They
    have also reduced staff by 931 staff.
        Now more than ever, VA needs to fully understand--have the
    comprehensive grasp on the skills and inventories that they
    have in their IT workforce, and at this time, they do not know
    that. They are not in a position to effectively assess what
    they need if they do not know what they have. That is an open
    recommendation that we have. That is first and foremost
    something that they need to do in order to answer your
    question.
        Ms. Budzinski. Okay. Thank you.
        Mr. Worthington, in GAO's review from July, VA noted that
    it faced challenges with implementing generative AI use cases
    due to a lack of sufficient technical resources and budget.
    Your testimony highlights this issue of cost as well.
        As it is currently funded and staffed, do you believe the
    VA is capable of implementing additional AI-use cases on top of
    these other modernization efforts that I have mentioned.
        Mr. Worthington. Thank you for the question, Ranking
    Member. I do think that we have the resources to implement
    high-impact AI, but it is a tough environment. Everything is
    competing for resources with each other. It is a matter of
    prioritizing those things that are going to have the most
    amount of veteran impact with the resources that we have.
        Ms. Budzinski. Okay. I guess I just go back to what Ms.
    Harris' recommendation, getting a CIO I think is really
    critical to helping to prioritize all of these different really
    important initiatives.
        Mr. Worthington, do you believe the VA's challenges with
    retaining AI experts and other technical employees may impact
    VA's ability to scale AI tools and other modernization efforts?
        Mr. Worthington. I definitely think having AI experts on
    the VA side will help make us a better purchaser of these
    solutions, and it is an important thing for us to do. We have
    invested a lot in trying to build this team, especially through
    partnerships with things like the United States Digital Corps
    and the Presidential Innovation Fellows Program. You want to
    lean into those sorts of partnerships to help us bring AI
    experts in, in addition to those that we can recruit ourselves.
        Ms. Budzinski. Okay. Great. Mr. Worthington, we are hearing
    reports of VA's ambient listening pilot will be rolled out
    across ten facilities by the end of this year. What is the
    Department determining a success for this pilot?
        Mr. Worthington. Thank you for the question. I will let Dr.
    Carey give you some details on that.
        Dr. Carey. Thank you for the question. We have established
    a series of criteria and evaluation as we roll this out that is
    focused on user acceptance testing, veterans' perceptions of
    the tool as its used in their ongoing trust, and the care they
    receive, and just overall performance of the tool. We will
    continue to monitor that during the pilot.
        Ms. Budzinski. Okay. Are you measuring clinician burden,
    and what are your targets?
        Dr. Carey. We are--I can take that for the record to get
    back to you with the specifics. In general, we are measuring
    clinician burden and getting clinician feedback both
    synchronously and through survey mechanisms to understand the
    impacts.
        Mr. Worthington. One thing I would love to add is the users
    of our generative AI tool that is deployed to the workforce as
    a whole, in a survey, 73 percent of the users of that tool
    reported that they were able to spend more time fully using
    their professional skills, and 68 percent reported increased
    job satisfaction. I do think that these tools are going to be
    value adds to our workforce to help them do more to serve
    veterans.
        Ms. Budzinski. Well, it seems to me that we are placing a
    massive burden on providers. That is a concern. From being an
    ambassador to the tool for veterans, ensuring the tools'
    accuracy, and then reporting and mediating issues as they
    arise, how is the Department working to be proactive about
    receiving feedback from providers on issues with this tool?
        Dr. Carey. Thank you. That is a great question. Just
    briefly, I want to recognize, it is so important to balance
    that survey response burden and burden on the clinicians that
    are also providing care. We have been partnering with
    clinicians on day one, designing this as they are the end
    users. We just have ongoing conversations with them about the
    best way to balance those competing things.
        Ms. Budzinski. Okay. Thank you. I yield back.
        Mr. Barrett. Thank you. I will--we are going to close here
    momentarily. I just have one quick question.
        On that listening and automation transcribing, is that file
    of that recording, is that deleted after it is transcribed? Is
    there some protection there to make sure that it is not
    archived or held someplace?
        Mr. Worthington. We do have procedures on that and would be
    happy to get that back to you for the record. I do not have the
    details in front of me, but, yes, we have got that accounted
    for.
        Mr. Barrett. Thank you. I will now yield to Ranking Member
    Budzinski for her closing statement.
        Ms. Budzinski. Okay. Thank you. I just want to thank the
    panelists for being here today to have this conversation. I do
    very much appreciate it.
        I do want to go back, though, Mr. Worthington, to a
    conversation we had earlier about the six VA employees that had
    been working with DOGE and a letter that Ranking Member Takano
    had written to the VA back in June. We have not gotten a
    response. We just want similar transparency around access to
    the data that those six employees had. That is veterans' data.
    I just want transparency and some additional information on
    that.
        Anything you can do to help us get a response back for
    Ranking Member Takano would be very appreciated. Thank you.
        Mr. Barrett. Thank you, Ranking Member Budzinski. I
    appreciate it.
        I want to thank our panelists and the members today for
    joining us for this important hearing. This hearing has made
    clear that VA has both made a tremendous--we have had both a
    tremendous opportunity as well as a serious responsibility when
    it comes do using artificial intelligence within the VA.
        VA has access to some of the best data and research assets
    in the world. I know Mr. Luttrell pointed that out in some of
    the questioning too.
        If used the right way, AI could help doctors detect cancer
    earlier, prevent heart disease, cut down on paperwork, and,
    most importantly, save veterans' lives and hopefully prevent
    veteran suicides in the process.
        Programs like REACH Vet show us it is possible when
    technology is focused on the mission, and we can improve
    outcomes. Let us be clear, AI is a tool, not a replacement for
    doctors, nurses, and care teams. I appreciate the VA
    stipulating that we are not trying to replace practitioners
    with AI tools.
        It can help identify risks earlier and provide clinical
    pathways, but it cannot and must not replace treatment or human
    judgment. That is the reason we send doctors to college, right,
    because we want them to be experts on what they are doing.
        Veterans deserve both cutting-edge technology and a strong
    medical team working together on their behalf. That means
    vigilance and self-responsibility--and a sense of
    responsibility are still required. If VA fails to safeguard
    veterans' data or to maintain transparency, trust will be lost,
    and progress is going to stall.
        This subcommittee will continue to hold the VA accountable
    to ensure that AI enhances care, reduces red tape, and
    strengthens--not substitutes--the human touch needed in
    medicine.
        I ask unanimous consent that all members have 5 legislative
    days to revise and extend their remarks and include extraneous
    material.
        Without objection, that is so ordered, and this hearing is
    adjourned.
        [Whereupon, at 4:23 p.m., the subcommittee was adjourned.]

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                             A  P  P  E  N  D  I  X

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                        Prepared Statements of Witnesses

                                  ----------

                   Prepared Statement of Charles Worthington

        Chairman Barrett, Ranking Member Budzinski, and distinguished
    Members of the Subcommittee, thank you for the opportunity to testify
    regarding VA's opportunity to use Artificial Intelligence (AI) to
    improve health care and services to Veterans. Your longstanding support
    of Veterans and their families is greatly appreciated. I am accompanied
    today by Dr. Evan Carey, Acting Director of the National Artificial
    Intelligence Institute, Digital Health Office, Veterans Health
    Administration.
        While the use of AI at VA is not new, recent advances in the
    capabilities of AI systems represent a significant opportunity for VA.
    Many of the most time-consuming tasks VA employees and Veterans must
    now complete manually could, in the future, be made dramatically faster
    and more accurate when assisted by effective AI-enabled software. VA,
    in partnership with industry, academia, and other Federal agencies, is
    working rapidly to seize this opportunity.
        VA's strategic vision is to make the Department an industry leader
    in AI that improves Veterans' lives by delivering faster, higher
    quality, and more cost-efficient services, with strong governance and
    trust.
        We have distilled this strategy into five execution priorities: (1)
    expanding AI access across the VA workforce; (2) reimagining high-
    impact workflows with AI and automation; (3) ensuring the most
    promising AI projects receive prioritized investment; (4) building an
    AI-ready workforce; and (5) running transparent and effective AI
    governance.
        To realize this strategy, VA is increasingly investing in AI-driven
    tools that enhance productivity, reduce manual burden, and improve
    service delivery to Veterans. In VA's 2024 inventory, we reported 227
    AI use cases, representing nearly 100 more use cases than in the 2023
    report. We expect this increase to continue in our 2025 report.
        These investments are yielding tangible results. In one highly
    anticipated use case, VA now offers an on-network generative AI tool
    known as VA GPT. Over 85,000 users are engaged with the tool which
    assists with basic administrative tasks such as drafting emails and
    summarizing documents and meetings notes. A survey of VA GPT users
    found that the tool saves its users an average of 2.5 hours per week,
    with more than 80 percent agreeing that it has made them more
    efficient.
        Furthermore, we have successfully piloted and scaled an AI-assisted
    software development tool called GitHub Copilot, now used by over 2,000
    developers within OIT and our contract partners. These software
    developers indicate this AI-assisted software development tool is
    helping them deliver capabilities faster and saving them over 8 hours a
    week. This includes faster development of Veteran-facing features on
    VA.gov, making it easier to refill prescriptions and apply for
    benefits, and the improvement of backend systems that accelerate claims
    processing.
        AI-augmented tools are also driving improvements in clinical care,
    with 82 percent of the over 200 use cases in VA's inventory coming from
    the Veterans Health Administration (VHA). VA's Stratification Tool for
    Opioid Risk Mitigation (STORM) uses machine learning to identify and
    mitigate the risk of overdose and suicide among Veterans prescribed
    opioids or with opioid use disorder. By summarizing patient risk
    factors, STORM identifies high risk Veterans for review by expert
    health teams. Health care teams reviewed the care of over 28,700
    Veterans identified by STORM in the past year alone, decreasing
    mortality in high-risk patients by 22 percent. Since its launch in
    2017, the REACH VET program has used tools like STORM to identify and
    bring clinical attention to nearly 135,500 Veterans, improve outpatient
    care, reduce suicide attempts, and decrease the number of mental health
    emergencies.
        Additionally, VHA has deployed 84 AI-assisted devices that have
    been authorized by the , including one that uses computer vision to
    enhance clinical outcomes such as early tumor detection. One VA study
    showed that using AI-assisted colonoscopy devices increased adenoma
    detection rates by 21 percent, which is associated with lower late-
    stage cancer incidence and reduced mortality.
        VA is committed to implementing innovative, AI-powered tools that
    advance health care for Veterans, improve the experience of care teams,
    and optimize VA's workforce. As part of this commitment, VA will pilot
    ambient scribe technology at 10 sites beginning this fall. Ambient
    scribe is an AI technology that listens to and documents the
    conversations between health care providers and patients. AI processes
    a transcript of the encounter to generate secondary outputs like
    clinical encounter notes and coding recommendations. It has the
    potential to transform health care by reducing clinician burdens,
    enhancing efficacy, improving patient care quality and experience, and
    engaging with clinical decision support services. Ultimately, it allows
    the provider to spend more time face-to-face with Veterans.
        As we progress, protecting Veterans' data privacy while responsibly
    leveraging AI's potential is a top priority for the Department. Like
    all software approved for use at VA, AI systems must meet VA's rigorous
    security and privacy standards before they receive an Authority to
    Operate. Additionally, consistent with the Office of Management and
    Budget memorandum M-25-21, our team is facilitating an agency-level
    review of each AI use case to ensure the tool meets the Government's
    standards for innovation, governance, and public trust. Each use case
    undergoes an AI Impact Assessment to identify and mitigate risks.
        Further, VA has established and is committed to maintaining an
    annual AI use case inventory. First released in December 2024, we are
    on track to provide an update to this inventory in December 2025. This
    inventory positions VA among the most transparent health care systems
    in the country regarding AI.
        Looking ahead, our focus over the next 12 months will be
    implementing our strategic execution priorities by expanding employee
    access to generative AI to 100 percent of VA staff, reimagining high-
    impact workflows, prioritizing investment strategy to high return-on-
    investment AI solutions, releasing new AI training opportunities for
    employees, and maintaining transparent and effective AI governance by
    ensuring 100 percent of VA's high-impact AI use cases meet the
    Administration's standards.
        Despite our industry-leading progress, VA acknowledges the adoption
    of AI tools presents significant challenges. Among them is integrating
    new AI solutions within VA's highly complex existing system
    architecture, and as a Government entity entrusted with Veterans'
    private information, balancing adoption of new and emerging tools and
    vendors with the Government's strict security compliance standards is
    crucial. Retention of AI experts is a challenge. Finally, scaling
    commercial AI tools will incur additional costs, making it an ongoing
    effort to align these costs with available technology funding. Cost is
    one of many reasons the Department encourages Congress to fully fund VA
    next year in lieu of another continuing resolution.
        In conclusion, VA remains steadfast in its commitment to harnessing
    the power of AI to improve the lives of Veterans. By strategically
    investing in AI tools and enhancing our workforce's capabilities, we
    aim to deliver faster, higher quality, and more cost-efficient
    services. While we acknowledge the complexities and challenges inherent
    in this transformation, we are dedicated to maintaining the highest
    standards of governance, transparency, and ethical use of AI. With your
    continued support, we can ensure that VA leads in AI innovation and
    sets a benchmark for responsible AI use in public service. Thank you
    for the opportunity to testify before you today. I look forward to your
    questions.

                        Prepared Statement of Sid Ghatak

        Chairman Barrett and distinguished Members of the Subcommittee:

        Thank you for the opportunity to testify. My name is Sid Ghatak,
    and for almost three decades, I have designed and deployed artificial
    intelligence and forecasting systems across finance, healthcare,
    pharmaceuticals, media, and government.
        I currently serve as the Chief Technical Advisor for the National
    Artificial Intelligence Association, the premier organization
    representing over 1,500 businesses in the advancement of AI, and am
    also the founder and Chief Executive Officer of Increase Alpha, LLC,
    where we use artificial intelligence to predict stock prices and
    license these predictions to hedge funds.
        In the Federal Government, I served in the General Services
    Administration for almost 4 years, where I was a Director of the Data &
    Analytics Center of Excellence. In that role, I co-authored the Federal
    AI Maturity Model 3 years before AI took the world by storm and
    contributed to previous Executive Orders on AI, specifically on the
    critical issues of data security and privacy.
        At Increase Alpha, I architected a predictive AI model that
    generates alpha once thought impossible--a deep learning system
    exceptionally accurate at predicting equity prices. Increase Alpha far
    exceeds multiple industry benchmarks, including accuracy, Sharpe ratio,
    and alpha generation. This solution is not based on Large Language
    Models but is a purpose-built predictive engine designed for a very
    specific need.
        I want to emphasize that it is entirely unrelated to the Department
    of Veterans Affairs and has no bearing on today's testimony. I mention
    it only as an example of how AI, when carefully designed with a clear
    purpose, can achieve exceptional effectiveness.
        Taken together, this diverse background--spanning academia,
    government, and industry--has given me the rare opportunity to actually
    build AI systems that work well in the real world. Because I have spent
    my career outside the orthodox worlds of academia, venture capital, and
    big tech, I am also not beholden to herd mentality. Instead, I bring an
    expert, independent perspective which is especially valuable now, when
    much of the world is caught up in the `art of the possible' with AI,
    when what is most urgently needed is a sober understanding of what is
    safe, practical, and ready to serve the public.
        LLMs like ChatGPT, Claude, and Gemini are a powerful subset of AI,
    but they come with their own set of problems, specifically in
    healthcare, where hallucinations and sycophancy on the part of chatbots
    can lead susceptible users down psychological rabbit holes. Which is
    why it's important to clarify that AI is bigger than just ChatGPT and
    its competitors.
        To use an analogy: the steam engine transformed society, fueling
    the Industrial Revolution. While steam power still exists today, it
    gave way to other forms of power over time. Until steam engines were
    used to create the first railroads, no human had ever traveled faster
    than a horse. This new form of transportation opened the world's eyes
    to what was possible, just as ChatGPT has shown the world the art of
    the possible with AI. But early train travel was dangerously
    unreliable. Accidents were frequent, derailments common, and thousands
    of lives were lost before rail systems matured into the safe networks
    we know today.
        The lesson is clear: revolutionary technologies will evolve and
    improve over time when the private sector and government work in
    collaboration. The same applies to AI.
        As the Committee gathers information on how to modernize technology
    at the VA, I would like to offer three pieces of advice from my decades
    at the front lines of building and implementing advanced analytical
    solutions:

            1. Expand the playing field: For the last several years, the
            world has been consumed with Large Language Models to the point
            where AI has become synonymous with it; however, that is not
            the case. Many other types of AI may have similarities to these
            models, but function very differently. Technologies that
            specialize in interpreting and understanding images, video, and
            audio, for example. Or technologies that are better suited to
            working with numbers and symbols instead of words. And new tech
            that has yet to be invented.

            There is an old adage that when you are a hammer, everything
            looks like a nail. The world has become so enamored with LLMs,
            and rightfully so. Interacting with them can feel magical,
            giving you the sense that they are real people, though they are
            not. This may be why little to no investment is being made into
            these other areas.

            At Increase Alpha, we have demonstrated clearly what can be
            done with other forms of Artificial Intelligence. I began
            building our models at the same time as the research underlying
            ChatGPT was published. I had also encountered the same compute,
            cost, energy, and reliance on Nvidia GPUs issues we still see
            today. I also took a different approach to conserve resources
            and focus on simplification using Predictive Intelligence,
            which led to lean AI models that use a minuscule amount of data
            compared to LLMs, and which are small enough to run on a cell
            phone.

            Over 4 years, the success of my models directly contradicts the
            notion that massive amounts of data--along with their
            associated infrastructural and operational costs--are needed to
            build AI solutions that are extremely accurate, innovative, and
            reliable. Not to mention that they also consume ever-increasing
            amounts of energy and utilize models that produce outputs that
            are often incomprehensible and unexplainable. I have proven, in
            one of the most competitive and challenging tech arenas, that
            modern AI does not require all this if it is designed correctly
            from the outset. The Administration, in its recent AI Action
            Plan, does not limit AI to the narrow definition of LLM and
            provides support for numerous types of technologies to be
            developed.

            2. Correlation is not Causation: The difference between
            correlation and causation is best understood through an
            example. There is a near-perfect correlation between the number
            of Google searches for the word `Nintendo' and the number of
            librarians in Michigan. It doesn't take a rocket scientist to
            understand that there is no actual relationship between the two
            trends.

            Why is this so important? Because AI solutions today, such as
            ChatGPT, are based on correlations, even if those correlations
            are nonsensical. It is why they hallucinate (make up answers
            based on nothing), and why they have an inherent bias. While
            they give the impression of understanding and reasoning through
            their rapid generation of coherent text, they have no idea what
            the words themselves actually mean. They excel at predicting
            the next best word based on a vast network of correlations and
            are even better at providing the user with the answer they want
            to hear, even if it's not accurate.

            To achieve true artificial intelligence, these systems would
            also have to know why the next word was predicted, which cannot
            currently be explained. They would need to know the truth
            behind every output. This is causation. That is how the human
            mind works. Until AI systems can understand and explain the
            `why' of their inner workings and outputs, and become reliable
            sources of truth, they will never be truly intelligent. I
            remain hopeful that I will experience this in my lifetime, but
            it has not happened yet, nor is it likely to happen soon.

            3. Data, Data, Data: AI is an engine that requires data. But
            not just any data. Accurate, functional AI systems that produce
            explainable and auditable outputs require vetted and cleaned
            data, which we feel 100 percent confident using. By some
            estimates, the Federal Government has more data than any other
            organization in the world.

            As a former Federal employee, I had the opportunity to work on
            projects that required this type of clean data to achieve their
            envisioned solutions. What I saw firsthand was the same thing I
            had seen in every other large organization. There was always
            more data than anyone realized. No one really knew where all of
            it was located or what it meant, and the sheer effort to
            gather, clean, and organize that data for proper use would have
            been enormous and cost-prohibitive.

            This is one of the key reasons many AI and data analytics
            projects fail. Unless an organization is willing to make the
            investments in organizing and cleaning their data, these
            solutions--to put it bluntly--will be like lipstick on a pig.
            They will not work over the long run, and even when they do,
            they will not be reliable because they are not explainable.

            We can see this already in current versions of Large Language
            Models, which are aptly named because they are built on
            unfathomably large amounts of language data. Some of it is
            factually correct. Some of it is factually wrong. Some of it
            has good intentions. Some of it is prejudiced, with built-in
            hate, discrimination, and the bias of their very human authors.
            As the old saying goes, garbage in, garbage out.

        What does this all mean for the VA and the well-being and care of
    our veterans? I can't claim to know. No one does. But I want to leave
    you with a prediction of my own. I believe we truly are on the verge of
    a revolution on the scale of the Industrial Revolution. So, if I could
    leave you with one idea today, it would be this: AI is actually much
    bigger than today's LLMs. And it is these technologies, many of which
    have yet to be invented, that will enable the VA to execute its mission
    ``To fulfill President Lincoln's promise to care for those who have
    served in our Nation's military and for their families, caregivers, and
    survivors.''
        In light of this, the Committee's work is vitally important to
    ensure that the investments the Federal Government makes into AI
    solutions will actually fulfill its mission. Our Veterans have given
    their bodies, minds, and very lives so that we all can enjoy ours, and
    we owe them more than our thanks and gratitude. We owe them the help
    and services they need when and how they need them.
        I am privileged to be here today, amongst my esteemed colleagues,
    and I look forward to answering your questions. Thank you for this
    opportunity.

                    Prepared Statement of Mohammad Ghassemi
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                       Prepared Statement of Carol Harris
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