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spe spe spe for

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spe for [Music] now come to order today marks our subcommittee's first hearing dedicated to exploring the transformative potential of artificial intelligence in healthc care specifically in the VA this powerful technology is being used in healthc Care

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Systems throughout the world as a physician and a 24e army veteran I have witnessed the evolution of Healthcare in both military and civilian worlds while progress tends to be incremental occasionally a process or technology

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emerges that pushes our boundaries out significantly the Integra ation of artificial intelligence or augmented intelligence in healthcare offers this opportunity AI creates possibilities to improve diagnostic accuracy predict and

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mitigate patient risk identify appropriate interventions earlier um um be a consultative resource for providers reduce the administrative burden and save money AI were told promises all while AI holds great promise the reality

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is that it is a new developing technology and we're still figuring out what is possible and practical and ethical AI a previous technology modernization subcommittee hearing addressed the pitfalls of AI particularly in data privacy today's

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hearing will focus on ai's potential to tap into that potential VA must first develop a strategy to use AI test applications and finally procure and Implement successful AI uh strategies across the organization as with data

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privacy care must be taken when using AI for clinical purposes if the data AI learns from is incorrect or biased it can make incorrect predictions that results in over or underdiagnosis or mistreatment these aren't just concerns

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they have happened in real life situations outside of the VA one promising AI technology for the diagnosis of sepsis an often fatal condition with rapid onset generated alerts for 18% of all hospitalized patients but completely missed 67% of the cases

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diagnosed this kind of error compromises not just efficiency but patient safety we will examine how VA is developing use cases Guided by various executive orders and how VA plans to implement successful AI use cases at scale across the

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healthcare Enterprise of course VA and VA Healthcare do not exist in a vacuum the VA is not an island AI efforts within the federal government are proceeding in a parallel while Private Industry is significantly ahead of the

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public sector even within VHA this subcommittee has heard that efforts to use AI are fragmented with visions pursuing individual projects that are sometimes duplicative of vha's efforts a priority of ours is to ensure VA moves

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forward with a cohesive strategy synchronized between the VA central office VHA Vis and VC's it is also critical that we understand how VA will choose assess and Implement successful AI projects at scale for the benefit of all veterans

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and in conjunction with private sector entities that have already been developing and utilizing this technology for some time we are joined by distinguished Witnesses from the tech industry Academia and the VA their Insight will Enlighten our discussion of

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the va's use of AI and its potential to augment VA Healthcare I believe in the promise AI offers and I look forward to hearing from our Witnesses about their efforts and vision for the future of AI to provide what's best for our nation's

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veterans with that I yield to ranking member brownley for opening statement thank you madam chair uh all of us gathered here today have no doubt heard something positive or negative about artificial intelligence and how it will

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change the way we live our lives in the coming years for most of us this is a very new technology and it will continue to evolve as we work to better understand how it functions and how we can apply it but it is also undeniable

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that this technology is already in use across various sectors of government including at VA and in private companies to ignore that fact and not support va's participation in AI research and implementation of this technology would

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be to allow VA and our veterans to be left behind today we will hear from are VA Witnesses about how they are approaching this technology identifying ways to implement it in veterans healthare and taking steps to ensure

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ai's benefits are Amplified and its risk minimized we will also hear from the companies and individuals working in this field about ways they see this technology can change how VA provides care and their experiences in engaging

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with VA on this technology so far VA is the largest healthc care provider in the country its implementation of AI technology can be a model for other Health Care Systems which makes it all the more important that we ensure VA and other AI users

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establish best practices procedures and guard rails early on in the implementation AI technology has the potential to revolutionize how veterans receive care and ensure Better Health outcomes providers using AI can potentially identify cancers more easily

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improve patient outcomes and identify how well treatments are working to manage chronic conditions AI can help providers review Imaging scans and focus their attention on areas where the technology thinks there might be an

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issue this will also allow patients to get results good or bad faster and it can help predict disease prog progression and potential applications allowing doctors to more effectively manage symptoms and apply preventive

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measures before the patient's disease progresses further it also has the potential to lighten the burden of administrative tasks for providers and allow them to provide more engaged and personable care it can help providers

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offer more targeted Outreach to Veterans who need additional support and it can help track and predict risk factor factors that will allow Mental Health Providers to intervene sooner for atrisk veterans however as with any new

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technology we must ensure that we are approaching its use strategically and deliberatively careful implementation will allow VA to establish and Trust in the technology and encourage veterans and providers to see AI as a tool to

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solve problems rather than a murky technology with potential risks it will be important at this hearing and as this committee continues to oversee va's work in this space to ensure that the patient experience is centered AI experts have generally

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acknowledged that AI will necessitate changes to workforces across many sectors some of these changes include applying AI to lessen provider burnout and improve the diagnostic and patient care tools available to providers we must ensure that we are taking

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advantage of these benefits to the highest extent possible however when it comes to Health Care removing or lessening the human element that providers offer in health care could be damaging for patient trust comfort and

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outcomes even as we find Productive ways for AI to be implemented we must take measures to ensure VA is continuing to robustly hire retain and I will emphasize retain and protect its clinical Workforce additionally we must

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ensure that as providers begin utilizing AI technology more frequently that VA can continue to recruit and train a Workforce that is able to use and troubleshoot the technology it's clear this is an exciting and productive time to leverage

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this technology as we strive to approach it with the same rigor and oversight we apply to all our work on this committee I look forward to work working with our partners from VA the private sector and Academia to ensure that we leverage its

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benefits to the maximum extent possible for the betterment of veterans care I look forward to hearing from our Witnesses today and with that Madam chair I yield back thank you so much uh representative brownley not so reare

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bipartisan agreement uh here um I'd like now to introduce the witnesses for our first panel Mr Charles worington Chief technology officer and chief artificial intelligence officer at the office of information and Technology Department of

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Veterans Affair Dr Gil alterovitz director of the va's National Art artificial intelligence Institute Department of Veterans Affairs and Dr Carolyn Clancy assistant under secretary for health at the office of Discovery Education and Affiliates Network

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Department of Veterans Affairs Mr worington you're now recognized for five minutes to deliver your opening remarks good good morning chairwoman Miller Meeks rank member brownley and distinguished members of the subcommittee thank you for the

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opportunity to testify today on the Department of Veterans Affairs efforts and exploring current and future possibilities of artificial intelligence my name is Charles Worthington and I'm the chief technology officer and chief

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AI officer in the office of information and Technology I'm lucky to be joined here today by Dr Carolyn Clancy vha's assistant under secretary for health and Dr Kil Gil alterovitz the director of the national AI Institute and vha's chief AI officer

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VA is committed to protecting veterans data while responsibly harnessing the promise of AI to better serve veterans while AI can be a powerful tool we must adopt it with proper controls oversight and security the department is taking a

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measured approach as we begin to scale AI solutions to ensure that we're adopting these powerful tools safely and aligned to va's Mission adopted in July of 2023 va's trustworthy AI framework outlined six principles to ensure that AI tools are

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are purposeful effective and safe secure and private fair and Equitable transparent and explainable and accountable and monitored this framework was designed to align with previous AI executive orders om memos and other

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Federal guidance as well as VA specific regulation and policy over the past several years VA has created the foundational guard rails it needs when considering AI tools have a significant potential to improve veteran Healthcare and

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benefits this foundational AI strategy has given VA a critical head start on developing policies to govern our use of AI and production I believe that creating this Clarity on our expectations will be critical for our partners in the private sector who are

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creating much of vaai Technology VA and other government agencies seek to use VA has long been a leader in healthcare research and at the Forefront of Technology we've led the way in various Innovations like the development

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of the first electronic medical record early adoption of teleah Health 3D printing and more to support va's adoption of AI in the healthcare setting VA established the national artificial int artificial uh intelligence Institute

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or the NY it's a collaborative effort among field-based AI centers and was pioneered by Dr alitz and his colleagues in BHA this network brings together data scientists and clinicians to enable AI research and development explore the

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application of AI in healthcare operations and test AI Quality Control Systems as reported in va's 2023 agency inventory of AI use cases VA has over 100 AI use cases tracked with 40 of those in an operational phase with

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examples spanning speech recognition for clinical dictation to computer vision for assisting with endoscopies to customer feedback sentiment sentiment analysis modeling most recently VA launched the AI Tech Sprint an annual

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requirement of the executive order 14110 this Sprint has two tracks focusing on how VA can use AI to address provider burnout by assisting with documenting clinical encounters and with extracting information from paper medical records

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by investing in these projects VA aims to learn how AI Technologies could assist VA clinical staff in delivering Better Health Care with less clerical work enabling more meaningful interactions between clinicians and veterans in closing the department

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believes that AI represents a general generational shift in how our computer systems will work and what they will be capable of if used well AI has the potential to empower VA employees to provide better healthare faster benefits

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decisions and more secure systems similar to other major transitions such as cloud computing or the rise of smartphones VA will need to invest in and adapt our technical portfolio to take advantage of this shift with the

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strategies policies and programs already in place the department will continue in its mission to protect the integ integrity and privacy of the data entrusted To Us by the veterans we serve Madam chair ranking member and members

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of the subcommittee thank you for the opportunity to testify before you today and to discuss this important topic my colleagues and I are happy to respond to any questions you may have thank you Mr worington we'll now proceed to

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questioning as is my practice I will defer my questions to the um end so I now recognize ranking member brownley for any questions she may have uh thank you madam chair my first question um is to you Mr uh Worthington thank you for

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being here um you know when it comes to technology and this committee's oversight of that and all of the uh initiatives and programs that the VA has uh technology has been very helpful on one hand and sometimes has stood in the

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way of meeting the goals that we have set out to do um and so as a consequence I you know I am always a believer that the VA should be leading the way as it as you mentioned you know some ways in which we have led the way that was you

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know a decade or two decades or three decades ago I I think the research is current don't get me wrong um but in terms of looking forward into the future obviously AI is going to be very very important so I'm asking the question uh

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to you is with regard to Ai and its use in the VA today right now where do we stand compared to Private health care teaching hospitals and the like uh thank you very much for the question and it's a good question I I think that we are

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doing our best with technology when we're using it to solve problems that are the most important problems for the agency and AI is no different I think that VA uh in my opinion we're right in the middle of the pack I would say at

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adopting these things uh I think that a lot of the health industry and I would love for Dr Clancy to chime in as well is at the early stages of adopting these new paradigms obviously many systems went all in on electronic medical

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records which is sort of the basis for a lot of what can happen now now that we've digitized a lot of the healthare data and I think we're at the early Innings of applying these new technologies to that data to deliver

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better healthare I think VA does have a number of uh of these tools that are in operations now uh but I think we also want to take a measured approach to make sure we fully understand how to monitor the safety of these tools as we deploy

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them more broadly Dr clency anything you would add um yes I would say where the middle of the pack or possibly uh even further up than that um the measured approach that Mr Worthington described is one that no system yet has put out in public

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or uh has figured out how to take all these steps in a very very careful way you know to balance benefits um while being very very attentive to risks and so forth and uh the chair gave an example of one that perhaps suffered

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from an excess of enthusiasm which was not to Patient benefit so I think there's a fair amount of caution all around but I would expect by virtue of our our size that in many ways we may actually be in the lead which would be a

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good place to be that would be a good place to be um M Mr um alter Fitz uh yes I apologize Dr alitz um so you've been you know you're you're new to the VA been in the private sector now for a while I think at Harvard and other

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teaching areas so what's your opinion on this uh thank you for the question Congressman uh and uh you know I think it's uh hard to Define it as uh that there's uh uniform progress what what I think we see is that in some areas for

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example devices um in some reason some areas uh such as devices medical devices we are uh well ahead uh medical devices we work through the biomedical engineering within VHA and then in other areas um that may require more

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complicated Integrations with different systems and involve uh collaborations that need essentially collaborations across the dep Department uh between different parts of the organization those are the ones that we're working

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toward uh you know Finding ways to do that uh efficiently at this time um the other area that we're we've been definitely ahead of is uh on this aspect of trustworthy AI a lot of the work that we've done ended up um being in or

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supporting work that we've seen in uh executive orders some legislation and so forth from the VA um and I think that's partly because we we do have that very special mission right with the veterans and and so we're especially looking uh

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at those aspects uh well ahead of time thank you very good thank you so uh Mr Worthington or Dr Clancy either one you know so what is your Department's plan to take the projects from the tech Sprints and pilot phase and the uh and

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Implement them as tools across the VA I I think it's an excellent question because I think we're all focused on how we're actually going to use this to help veterans and we are very focused on not just the outcome of the tech Sprints but

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some of the other steps that we need to take to make it possible for VA to adopt these at scale so things around uh the Contracting approaches uh the underlying technical infrastructure to support the hosting of these tools or the purchase

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of them if they're hosted by a third party uh as well as the workforce the you know there's work that we're going to have to do both on the AI practitioner side to make sure we have a Workforce that understands how to manage

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these tools but also on the user side I think there's a lot of training we're going to need to do with our staff about how to effectively and safely use these tools and we're starting to make investments in all of those areas now so

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that we're ready to receive uh promising insights from things like those Tech prints thank you uh I yield back Madam chair thank you representative brownley the chair now recognizes Dr Murphy for five minutes thank you Mr chairman and

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thank you all for coming today this is uh this is kind of Gold Rush material I think that we're coming literally on the Vanguard of all of this and so you mentioned Medical Records I remember kicking and screaming about 18 years ago

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when we would literally spend about an hour just trying to put in an order set so come a long way since then so we still just literally on the Vanguard of this are going to have to go a long way before this is really streamlined

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integral to to Patient flow just a couple questions um Dr alterwitz what is the are we still using Cerna at the VA I'm going to all right maybe Mr wory sorry about that so the electronic medical record mization project which is

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to migrate our Vista instances to use the dod's uh Oracle Oracle Health product which was previously known as Cerner yes that project is underway and I believe there's maybe four or five sites that have currently migrated all

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right so that a couple maybe it was months ago um we had a a hearing on Cerna and then one of the gentan mentioned it would be probably 5 years until it was fully functional and all these other things so here we're trying

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to walk walk and chew gum at the same time we're trying to get our you know providers to really even learn the system much less now try to integrate artificial intelligence this is really going to be difficult and very very

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challenging we had a a witness a few months or a month or so ago who said that the the efficiency now for clinicians was 60% compared to academic medicine which is normally about 60% compared to the community so this is really I think going to be very

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disruptive in the learning process um to clinical flows so um can you expand a little bit what you meant with the dod are we now having a little bit better communication between our two Health Care Systems DOD and yes the the goal of

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that project is to actually have both systems use one medical record system and so that's uh underway now and I think you're raising a really uh critical point which is that many of these AI solutions to really be truly effective need to be carefully

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integrated into the existing workflows so that they actually reduce burden and reduce you know the number of clicks and not add you know yet another thing that the providers need to check or open to I I will tell you I still have my very

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very I think well-founded concerns about Cerna being able to handle this it's just it was a system made for smaller hospitals and here you talk about the biggest Health Care system in the country um I just I worry about their

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ability to want even deliver a regular product much less an AI product um you know one of the one of the best things I thought about residency is the fact that it's kind of like a buffet line you had five six S 8 nine 10 attendings and

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while you had to rotate with each one you took a little bit about what they learned a little bit about they learned but if you ask the same question to 10 attendings oftentimes you get 10 different answers and this is where the

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problem with bias is going to come in we learned that bias especially the public learned about medical bias during the pandemic we had one rule one person making the comments one person doing this so this is going to be a tremendous

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issue for us yeah I'm a urologist I I just recently looked at the aua's one of their quote guidelines remember what guidelines were they were saying hey think about this and now I'm hearing I'm seeing the clinician should should

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should so this is I think it's very problematic when this happens and so we're when we're rolling out AI products um and it's saying should yeah we this going to be a massive liability concern in my opinion because what if

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you're staring in front of patient and the AI generator says should and you're thinking I don't think so and then God forbid if something else were to happen who's liable this is a major major concern Dr Clans you want to speak to

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that um yes so I'm sure Dr Murphy that you've heard that many Physicians prefer to use the term augmented intelligence as opposed to artificial intelligence and another words the human in the loop is uh quite important so by way of

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example right now in research we have teams working on developing artificial intelligence predictive rules to try to identify which veterans uh are likely to be uh to do well after an initial definitive treatment for prostate cancer

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and which are likely to have more far more aggressive disease and need much more frequent monitoring and so forth uh there is no plan um to and we don't know enough to actually even get anywhere close to should but it's it's an

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incredible opportunity yeah I saw that com in a guideline and I'm like I I was dumbfounded we can't say that in medicine we can't say should have to and all these other things that takes away absolute clinical aspect you know I

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could I could ask you questions for four days because this is such a a target-rich environment one of the things and I'll just end this you know the medical records writing these are the bane of our existence I spoke with

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the head of another company I won't say here um and their thought was you could walk into the room it would have a microphone you're just talking with the patient it's a simp assimilating what you're saying what the patient's

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responding to and then you just tell it you know I'm going to order this bam bam you walk out of the room and the notes done the orders are done the paperwork's done that would be a Quantum Leap Quantum Leap to addressing uh physician

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provider burnout so well that's that's exactly what we're testing sir in this Tech Sprint that Mr Worthington referred to Y and having seen one of these tools uh demons demoed live it was quite amazing um and we were going to be

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testing all of this in Simulation Center uh in Orlando so that we people can figure out what the workflows are we've actually looked at one company's product because at that point in time June of last year that was the only one they

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thought was um ready for prime time and I have to say the teams were wildly excited like when can we start yeah that's a big time I'm I've exceeded my time just is just remember AI is not going to take over my scalpel so all right

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maybe thank you Dr Murphy the chair now recognizes representative buzinski for 5 minutes thank you madam chair and thank you ranking member uh for holding this important hearing today I want to thank the witnesses as well from the VA for

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participating really appreciate that as we have heard um this morning there is so much potential and I believe that in AI uh to better serve our veterans and especially the veterans that I'm honored to represent in Central and Southern

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Illinois which are predominantly rural veterans um but a part of the nature of artificial intelligence is that it is constantly changing um which can lead to challenges when trying to implement or scale up the technology so my first

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question is really for the entire panel and if it's okay we'll start though with Mr Worthington um what steps um is the VA taking to Monitor and keep up with the emerging Tech I'm sorry emerging research on artificial intelligence uh

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thank you for the question congresswoman we have a a really robust partnership with our colleagues in vha's innovation group as well as the national AI Institute which is I would say constantly looking at the emerging research on thisch technology and even

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doing some of its own research uh and I think you know my part of the VA kind of steps in once things are getting past that research phase and into something we want to start testing with real veteran data or real clinical use cases

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uh and then as we find those examples that are most impactful then we bring them into operations uh in a way that is somewhat similar to how we would operate other it systems uh we're following those same security and privacy policies

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that would govern our use of Veteran data in other cases as well maybe defer to the other panelists if they want to talk to how we're keeping up so a couple of other efforts first a lot of our currently funded research from the re

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office of research and development does not have ai in the title but um by way of the topic that is being focused on whether that's cancer research or other problems uh they are testing strategies to try toe predict who is likely to do

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the worst um we saw a lot of this as well during uh the acute phases of the pandemic I'm trying to get past saying we're done uh because we're kind of not um and we were able to predict for example which patients uh hospitalized

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with covid were most likely to die within the next several months because those would not be the people you'd want to be discharging first you want to be attentive to detail and so forth we also have a team keeping up with the

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published literature and things presented at meetings and so forth so um there's so much we need to know about the safe and effective part that Mr Worthington referenced uh that we are very very excited about it and don't want to leave any stone

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unturned um so I just wanted to kind of say um actually it's a a quote that I heard from a former VA person that really uh you know research is kind of needed in a couple places right there's uh there's kind of a need for for

0:41:57

research to ensure uh that operations are really based on on science right uh and then the reverse is also true in some sense uh for research to be successfully translated right um into operations you you have to you know push

0:42:13

uh forward on that so connecting research and operations is a very important um kind of mutually uh kind of symbiotic type of thing where they work together to create the best product on the operation side the best research that can actually be useful and

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leveraged and so interacting from the beginning is something that we we do at the va8 to really make sure that um all the work that we do can be useful for the veterans and can I just ask have you found um in this research and in this

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collaboration and Partnerships you have um any ability specifically for AI to address some of the gaps in VA care for Rural uh veterans in particular have you had any specific takeaways from the research uh thus far I

0:43:02

guess sorry I think the VA has a number of programs to try to address that Gap including our teleah health program and overall I think anything that can make our system more efficient at identifying which patients are most in need of

0:43:13

specialty services for example could assist with things like our teleah health program and getting those right uh exact right uh experiences to the patients that need them uh beyond that I don't know that there's specific AI uses

0:43:26

in the the rural space um but it's a very interesting question well I I'll simply say that uh we have a very substantial initiative in investment in Precision oncology so this focuses on lung cancer and prostate cancer and from

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the beginning launching this four or five years ago you know the overarching motto was leave no veteran behind so we're now up to about 75 tonology clinics and also working through the extent to which we can engage those

0:43:55

veterans in research without making them come a phenomenal distance to the research intensive institution so there's a lot of work going on there and I know that cancer is a very very big issue for Rural communities I mean a big

0:44:08

fear yeah thank you thank you very much I'm out of time so I'll yield back thank you thank you representative pazinski the chair now recognizes representative rosenell who is the chair of the subcommittee on technology modernization

0:44:21

representative Rosendale you have five minutes thank you very much um chairwoman Miller Meeks for holding this hearing allowing me to participate today I appreciate the witnesses for being here good to see you folks again I

0:44:31

chaired a hearing last month in the technology modernization subcommittee titled the future of data privacy and artificial intelligence at the VA this is an important topic and something the VA must get right I'm grateful that the

0:44:44

uh committee is giving artificial intelligence the necessary attention that it needs Mr worington and Dr alterovitz during last month's hearing I asked you whether you think the VA has a responsibility to notify veterans when

0:44:58

their health or personal information is fed into an AI model or whether analysis that affects them was done by AI rather than a person and everybody seemed very agreeable and supportive of that that that we actually had this disclosure and

0:45:13

that the veterans were uh that question was posed to them when are you going to put that notification and informed consent procedure in place thank you for the question uh we we are working with our VHA ethics group right now uh to

0:45:30

better understand what the approach should be on this topic obviously this is kind of an emerging topic uh as you stated in in the prior hearing and so I don't believe we have a specific time that we are aiming for to implement this

0:45:43

but we are very aware of this uh this issue and I think it's one that's spoken to in the executive order as well uh our thinking right now is that the use case inventory is kind of the basis for which we would want to make those disclosures

0:45:55

and obviously the use case inventory is a pretty technical document so we're going to need to do work to make that understandable to veteran patients so that they can understand how the VA is using Ai and how their data might be put

0:46:08

into those models and that's that's fine and good okay I know you're working on this the the problem that I see is that you're literally putting the the cart before the horse you're you're utilizing okay you're utilizing Ai and you're not

0:46:22

disclosing it to the veterans you're not giving them a choice and and that is dangerous it truly is it's dangerous and it's and it's uh it's dishonest there's there's no really industries that are allowed to be to be utilizing different types of of

0:46:42

techniques and tools okay without the consumer being notified of what those techniques and tools are and how it may impact them and so I will reiterate this needs to be a high priority you're utiliz izing AI at whatever degree at

0:46:59

whatever level and and it truly the the veterans need to be aware of that and they need to have that consent and and to continue to utilize it is not right um do you have information that would show that the analysis of any type of

0:47:16

testing whatsoever can be done more accurately by by AI rather than a doctor's than a doctor's uh be eye shall we um we do not have that information and I think it's going to be hugely important um women recently have been

0:47:32

offered the opportunity to spend another $40 to get an AI enabled uh mammogram rating and you know to a person most of the Physicians uh interviewed for this article said I have no idea if this is worth the money some people coughed up

0:47:47

$40 and others uh did not um I did want to get back to your very important question about ethics though we I'm just quoting from my colleague we're developing processes and standards right now um and but the first step we thought

0:48:02

was a very broad ethical framework about protecting the privacy of Veteran data um and that cuts across Ai and everything else so we'll be happy to follow up with you as we progress through that our lead ethicist in VHA is

0:48:16

uh really terrific and and again I I appreciate that and I do believe that you're working on that the the problem is that you're already utilizing Ai and the veterans are not they do not receive informed consent Mr Worthing during the last hearing I

0:48:32

asked you whether anyone the VA ever rejected an AI use um and if so why uh you took the question back if they're not getting consent if they're not getting disclosure then then it's probably not likely that they are but have you already had any

0:48:51

veterans rejecting the use of AI even without this consent I am not aware of specific examples of that I I think that you know in many of these cases the AI we have in operations today is tied to like an FDA approved medical device so for example we have a

0:49:09

product called clear read which is a uh tool that assists with Radiology scans uh chest CTS uh so these these uh features are being added to existing products incrementally and in many cases being adopted and I think there's this

0:49:24

new interest of the AI technology with you know a broad definition of what would constitute Ai and I think as as Dr Clancy mentioned this is a a topic that I think our ethicists are going to have to kind of understand what new uh

0:49:38

requirements should we should we create uh versus what can our existing guidance on you know standards of care and other sort of disclosures uh how much will that sort of cover the basis okay thank you and Madam chair I say I'm out of

0:49:51

time I yield back thank you very much representative brownley had another another follow-up question so I'll yield to her thank you madam chair I appreciate it so I just wanted to ask one last question I don't know off hand but I

0:50:04

think the majority of medical centers are associated with medical schools and teaching hospitals so I'm wondering are there Partnerships out there working um and is that happening really across the board with medical centers and uh medical

0:50:23

schools um absolutely absolutely so we are affiliated with literally every single medical school in the country and many many other programs associated with other health disciplines which is just an awesome uh asset to have uh in the

0:50:38

research space um many of our docs about 60% and it's a higher number of those who are active researchers um actually have dual appointments with an academic affiliate um so yes there's a lot of collaboration going on and uh we look

0:50:53

forward to more of that here yes thank you the chair now uh recognizes uh representative rosenell for an additional minute thank you very much Madam chair I do appreciate I just have one more quick question Mr rothington the VA holds an unparalleled

0:51:11

wealth of V veterans data uh far too many companies are already interested in monetizing that that this information we've seen them actually buying it to skirt around the privacy laws okay especially when it's when it's in some

0:51:25

government agencies doing so uh and it seems to be getting even more tempting AI companies compete by consuming the most data to train their models and some of them already have covered nearly everything on the public internet how

0:51:38

are you going to protect veterans Health Data as it becomes more and more lucrative prize for these companies to get their their hands on yes it's a excellent question congressman and we we believe very strongly that protecting

0:51:50

veterans data is pretty much job one especially in office of information and Technology I think we're lucky that we have a lot of existing policies around how veterans data can be used and how it can't be used and we would expect that

0:52:03

those would all continue even in this AI use case uh it will be really important that all of our vendors understand which is the case today but that there's so are you putting are you putting language in place in any agreements with your

0:52:16

vendors to make sure that that that that information is protected and not monetized yes I I believe that our existing contract vendor relationships already have Clauses that say they can only use this data for very specific

0:52:28

reasons if indeed they even have access to it often times this data is stored on a VA system it's not given to a vendor uh to have in their system at all uh but I I do think that because as you mentioned the value of this data is

0:52:43

uniquely um increasing in the age of AI I think it's something we want to look at to make sure that we're very very clear that um you know the data can't be used for any purposes other than what's in the contract thank you very much

0:52:54

Madam chair thank you very much much I yield back thank you the chair now recognizes herself for five minutes um and I appreciate the great questions uh by our members both on the uh 1 million in prizes uh for the conclusion of the

0:53:07

tech Sprints um and I appreciate your answer to that a follow-up uh question to that is whether there'll be barriers to implementation for these Technologies Dr Worthington uh Mr Worthington I wish wish I was the doctor uh sometimes uh

0:53:22

but but yes I think that um Contracting for technology is uh obviously a pretty complex topic and there's a lot of rules around how that works in the government uh and that's one of the things that we you know we we work on being good at uh

0:53:37

but there are things like uh fed ramp for cloud services which is a policy um designed to ensure that cloud providers have some of those data privacy protections that we just discussed uh one of the challenges we see in the

0:53:50

health space in particular is that while VA is a big healthcare provider in the scheme of the American healthcare industry were relatively small and so often times many of the healthcare uh tool providers they're not really

0:54:02

familiar with fed ramp as a compliance regime that they would be focused on now they have a number of other compliance regimes from the health industry that they focus on uh but fedramp is not often high on that list uh so that's one

0:54:13

example of some of the challenges we sometimes have at doing Acquisitions of Enterprise Tools in this space in followup to um Dr Murphy was an excellent question I had this same question on my mind with without the full implementation and the hiccups uh

0:54:30

that the VHA has had in implementing its electronic health record that certainly is going to impact and I think delay your implementation of appropriate AI in to the VA um and perhaps I'll ask that question a little bit more on the second

0:54:46

panel um suicide prevention is a top priority for me for this committee uh and uh for the larger this subcommittee and the larger commit committee and what is the VA doing in regard to using AI to better prevent or predict veteran

0:55:00

suicide and this may be related to my comment about ehrs and how are we ensuring these tools are the best ones available on the market Mr wton yeah yes great question I'll just maybe point out two examples of our current uh use of AI

0:55:14

and operations one is uh we have a model called reach vet which is designed to predict the veterans that are most at risk for suicide as an outcome and that uh information then can be used to inform the way that the doctors follow

0:55:27

up with them or the the treatments that they prescribe when they're seeing them uh that model is in operations now uh to provide another example we have a natural language processing model that is looking at comments that are coming

0:55:40

in through our basically our customer experience listening uh so most of those things are like you know I went to the VA and the parking was slow or whatever but occasionally those comments will indicate that this veteran might be at

0:55:52

risk or need you know help maybe they're indicating that they're having uh homeless problems uh so this NLP model can flag comments that might be particularly concerning for followup by a professional that can read the comment

0:56:04

themselves and decide if some other action is warranted so those are just a couple of examples of how we're trying to use these tools to help the VA with that mission yes so if I can just on followup to that uh given how important

0:56:18

this issue is if there's a flag are you working with the clinical side to make sure that that is addressed in immediate fashion we have veterans have who have committed suicide in the parking lot of a VA hospital because they were denied

0:56:32

care or thought not to be suicidal yes so rich fet that Mr Worthington uh referenced is uh focused on Veterans who are enrolled in our system and we have seen a decrease in suicide attempts and a subsequent decrease in all cause

0:56:47

mortality hard to pinpoint that and say which is associated with suicide or not we're also working with um external contractors to use various uh types of AI uh working with veterans who are not enrolled in our system because when we

0:57:02

give the numbers about veteran suicides and think about what our responsibility is it's all veterans not just those who are enrolled in the veterans Health Administration thank you and Mr worington even though the VA has a

0:57:16

published um and uh my opening remarks called augmented augmented intelligence AI strategy it's difficult to find guidance on how that strategy is implemented and how VA is fairing against other uh against each of their

0:57:30

four stated objectives is the VA going to publish key performance indicators so that us here in Congress and the public can actively see the progress VA is making in the AI space yeah I think that's an excellent suggestion we will

0:57:43

be updating the AI strategy uh in the the coming year and I think having kpis would be a great idea thank you very much um there are no the representatives here at this time so on behalf of the subcommittee I want to thank you for your testimony and for

0:57:59

joining us today you're now excused and we'll wait for a moment as the second panel comes to the witness table for for spe welcome everyone that's my signal I'm going to thank you all for uh participating in today's hearing um our

1:00:22

uh Witnesses on our second panel Mr pan N I said it in my brain uh natar Rajan an author on the topics of artificial intelligence and Healthcare and machine learning Mr Gary Velasquez chief executive off officer uh and uh

1:00:40

president of kahi tavito Mr Charles Rockefeller co-founder and head of Partnerships at Cura patient Dr uh David Newman toer director of the Armstrong Institute Center for Diagnostic Excellence at Johns Hopkins University

1:00:54

School School of Medicine Dr Nat Rajan U will uh deliver his opening statement you have five minutes chair Miller Meeks ranking member brownley and members of the VA Health subcommittee my name is Prashant nren and I have problems pronouncing my

1:01:14

own last name half the time I'm an author of four books on Health Data Ai and cancer I have more than 20 years of experience in building electronic health records including Sona uh also Medical Imaging and building AI systems at scale

1:01:31

I have brought about 100 AI applications and use cases to life with my customers and my teams for the last 8 years I've been volunteering as uh industry adviser on data science and AA AI at the San Francisco VC and UCSF where we have been

1:01:51

developing expert solutions for detecting traumatic brain injury specifically using head C in my daytime I work as vice president of health and Life Life Sciences at h2o.ai which is a leading open-source generative AI company it is my privilege to join you

1:02:10

for this important hearing on AI in the VA it is a cost that is close to all of our hearts and is happening at a pivotal time for our veterans the clinicians who serve them and our industry as a whole AI is already bringing value to Health

1:02:27

Systems pharmaceutical companies and various organizations in the public sector it's happening right now generative AI provides a lot of new options it does that and more by augmenting and amplifying The Human Experience generative Ai humanizes and

1:02:48

empowers by democratizing access to actionable insights using plain English plain Spanish on any other language of your choice any patient can rapidly develop a personal health AI where each individual creates the AI that they need

1:03:06

in addition to what is created for them by others veterans can now use generative AI to better manage their health and life similarly clinicians can leverage gen to address burnout reduce human errors and find the needle in the Hast

1:03:24

stack of expanding scientific knowledge allow me to illustrate with an example of 11-year-old who's using generative AI to turn her baking hobby into collaborative Solutions and new value for her classmates she does this by creating new

1:03:43

AI business applications and agents and she did not even know what these meant a year ago she used generative AI to ask questions about sconed recipes she then tailored them for her dad's taste which is not easy she customized them for her

1:04:00

various users preferences and created a new data set that combines unstructured data across PDFs web content and her own recipes to create new information new recipes and is now in the process of using generative AI to create her own app to

1:04:18

take mobile phone ERS if a 11-year-old can do this for something as as basic as creating recipes imagine what our veterans and VA clinicians can do with the same technology to address Health outcomes and other issues of much greater

1:04:38

importance in my written testimony I have provided numerous examples of patients and clinicians as AI trainers AI creators and empowered users I'm happy to review these examples in Q&A or post this hearing so how do we create

1:04:55

this new empowering future of bottomup innovation based on our experience so far with the plot program in creating empowered Patient Advocates and researchers we have some proven best practices in place here are four things

1:05:10

we need to do together one recognize AI Fidelity which is the value of Health AI being determined by its user veteran clinician or administrator in the cont context of its use two recognize and encourage the fact that AI use cases can

1:05:30

come from anywhere both within and outside of the Four Walls of an Eva facility three we need to empower veterans to develop the tools they need to address their personal problems we need to create public private Partnerships with appropriate data tools

1:05:50

upskilling and deployment option underscored by a veteran first AI ownership of their assets finally the personal and provider Health AI that I describe in my AI collaborative are new ways of bringing AI to life in the VA

1:06:05

hence veteran created models and AI apps should be treated with minimally prescriptive regulations and should encourage the use of Open Source thank you again for inviting me to testify I look forward to working with you the VA

1:06:19

clinicians and our veterans to solve long-standing challenges and create new opportunities thank you very much Mr vesas you are now recognized for 5 minutes to deliver your opening statement thank you there we go thank you chair Miller

1:06:37

Meeks ranking member of Bramley and they see members of the committee I appreciate the opportunity to come and speak this morning on the use of AI at VA I possess Advanced Technic degrees with over four Decades of experience

1:06:50

operating large Healthcare analytic companies National Health Health Plans large medical centers and an international clinical research organization also want to acknowledge the federal government including VA for its AI initiatives which have leaned

1:07:05

into the use of ML and AI to improve the health of Americans my company was privileged to participate in the early stage of ml programs for operation warp speed while we specialize in Precision Health we perform at great speed and

1:07:19

scale however I would say the path from diffusion to operations has been somewhat clouded we my company when we take on a project we intend to deploy our solution not a wish to deploy our solution and I think that's a little bit of a challenge

1:07:38

we see so everything we do has an intent to deploy in the private sector today we stand on the precipice of transformative ML and AI possibilities to empower VA beneficiaries reduce stress on providers improve patient outcomes and deliver

1:07:53

personalized Healthcare care however must we must ensure we don't become blinded by following the next shiny AI announcement we should focus on the right use cases for AI and more importantly use cases that can rapidly

1:08:06

improve meaning today healthcare for our veterans I want to describe two use cases that can bring to life immediate benefits of machine learning for veterans the first use case covers completed work at the VA where our models have been tested to predict

1:08:21

beneficiary level disease progression my Second Use case covers the ability to predict clinical conditions related to genetic mutations polymorphisms that may have resulted from toxic exposures so actually using mutated DNA to predict

1:08:35

future clinical conditions an American hero raised me my dad in listed in the Army at age 15 if you saw his pictures he looks like he's 12 he received uh two silver stars at age 17 for his service in Korea and I got to see him when he came home both with a

1:08:52

physical and in inv visible wounds and he raised me and he's dealt dealt with that invisible wound of post-traumatic stress for many many years then I got to see him age and he wrestled with his post-traumatic stress and at the end of

1:09:05

his life he wrestled with my mom's cancer while he's trying to manage his CKD and his diabetes as we all know combat veterans are selfless I think being in combat makes you selfless and he was selfless with my mom my dad chose to focus on my

1:09:21

mom's health instead of his and unfortunately we didn't know the actual state of his health he passed from The Unseen unknown complications related to CKD and diabetes that choice my dad made doesn't need to be made today we

1:09:37

have the tools we have machine learning tools to help VA providers his provider to identify predict and communicate that disease progression not just to the beneficiary but to the providers and to the family when we have this tool deployed

1:09:52

at the VA we can help help event and their family navigate the conditions of age as you all know the average age of a veteran is increasing every day it's now 68 so this machine learning capability can assist these older vets their family

1:10:05

and the providers with specific insights into their conditions these insights can also reduce the number of touches of a medical chart relieve administrative burdens and reduce the cost of higher Acuity care with toxic exposures we

1:10:18

recognize the pressing concern of adverse health effects and stressors from toxic exposures among our veterans and the family by leveraging ml techniques we can unravel this complex interplay of genetic mutations and illness we can

1:10:31

enhance our understanding of how these factors influence Health outcomes and enable timely earlier diagnosis and treatment for example my company's Chief uh medical officer his son and daughter fly jet fighters both techn knowledge

1:10:46

have been exposed and they have three simple questions for us today what are my risk possibil probabilities for future medical condition i s what type of diagnostic testing should I get for these conditions and what's the

1:10:59

frequency of those tests they know they signed up for the military for those risks and they're fine with it but they have these three basic questions and with machine learning we can quickly answer these questions with the support of Congress

1:11:12

VA can be a Cornerstone in delivering AI enhanced services to improve human health not just veteran Health given va's Mission operations and Rich data repositories few other organizations can deliver this on this objective better

1:11:25

this concludes my remarks and I'm pleased to answer questions you may have thank you Mr Rockefeller you're now recognized for five minutes to deliver your opening statement great thanks uh thank you madam chairman um good morning ladies and gentlemen my

1:11:46

name is Charles rockfeller and I'm the co-founder and head of Partnerships for cure a patient it's a real honor to have been included uded today in this very important discussion a coincidence I happen to feel more historically

1:11:58

connected to the VA because I heard about it being discussed at the dinner table since age 12 my father sat in the Senate VA committee for 30 years either as a member or its chairman my other uh two co-founders of the company are

1:12:12

longwin who's been supporting the US government and its AI Endeavors since its very Inception 20 years ago and Dr sartha Mukerji a pure priz winning oncologist first some information about our platform and our technology um our

1:12:29

its features mostly fall into three categories and have been designed specifically to be able to support patients providers and administrators to deliver care efficiently and seamlessly these features create seamless support for veterans and reduce

1:12:43

worker burnout as has been discussed many times before one of our first successes came while working with operation warp speed where we helped provide equ Equitable access to Critical Care while also allowing our brave

1:12:55

Frontline workers relief to focus on the job at hand I'm proud to say that we received a Red Cross Heroes award for this service with all that as a foundation I would like to shift my focus to our work with the va8 in particular and more

1:13:11

specifically um cure patient our company first came into contact with the VA in 2019 via the uh the the N Tex Sprint as as as you all know what it is um we won that and I'm proud to say we redeemed uh the future of healthcare although they

1:13:31

might have been generous with the title since it was the first Tex print um but I I think we are uh today I'd like to highlight fe uh five key topics from our experience with the VA and each creates the foundation not just to innovate but to do so

1:13:47

responsibly and at scale which I know is a two continuous themes number one data privacy and security we have dedicated over two years and thousands of hours along with significant resources to gain fed ramp certification as part of this

1:14:02

commitment we've implemented 421 uh National Institute of Standards and Technology nist security controls and those are of course the highest in the industry the effort has been led by collaborative and cross functional

1:14:17

Endeavor um significantly propelled by the leadership of Charles Worthington and ch Gand Curtis uh Dr kolen Clancy of course and and first um before all of them the initial Direction and support from from uh Dr Paul tibits our commitment to Fed ramp

1:14:36

reflects our dedication to protecting our uh veterans sensitive data number two seamless um integrated and Veteran Centric experience our work is centered on creating a seamless and userfriendly experience for both veterans and VA

1:14:51

staff then they'll both work together better we're thrilled to report that we've successfully completed five out of our six targeting Integrations granting us the bidirectional ability to both read and right to patient records number

1:15:04

three clinical application of AI our collaboration with the VA facilitate facilities in Long Beach and DC uh Long Beach California has been a Cornerstone of our efforts where established AI oversight committees and policies are already enhancing our work

1:15:22

these committees our Technologies integration starts with addressing uh long covid um which as you know is in the news recently for being much more prominent now this condition with its broad impact on the body provides a unique opportunity for wide

1:15:37

ranging engagement using our Solutions and there's a benefit to this as well because our Solutions are designed to tackle other chronic diseases and on a larger scale as well uh number four responsible AI these Pilots would be deployed at n centers

1:15:54

and will be available later across the entire VA The Long Beach and DC VA Medical Center teams led the work it enforces compliance with trustworthy principles as defined by executive order 13960 incorporates nist AI RMF and all

1:16:13

non-binding principles within the White House AI Bill of Rights the team has stated that the AI system we created uh our our name care patient shall only move forward and can only move forward with the full approval of these bodies and the more it's used

1:16:31

which is important to realize the more it's used the smarter it becomes number five Contracting we're optimistic about the benefits of enhancing our Contracting approach which promises to be a positive change as technology especially AI

1:16:47

advances rapidly navigating the complexities of traditional Contracting becomes a growing Challenge and I'm nearly done uh Often by the time firm fixed price contracts are executed the technology is maybe it's three years

1:17:02

later the technology has already been replaced or or Advanced so it's vital to consider alternative Contracting methods and I would call upon con Congress to make this um a priority um as well as as funding to turn these

1:17:18

opportunities into real benefits for veterans um we we The leadership's Works of the VA has resulted in a soon to be Mission ready system that can greatly apply advancements and AI not only in theory but directly to our veterans and

1:17:33

support staff thanks very much for your time and uh I'm happy to take questions thank you thank you Dr Newman toker you're now recognized for five minutes to deliver your opening statement thank you uh chairman Miller Meeks ranking

1:17:48

member brownley and distinguished members of the subcommittee thank you for the opportunity to address Congress on this critically important topic of artificial intelligence and healthc Care at the VA in support of our veterans my

1:18:01

name is David Newman toker and I'm a physician scientist with doctoral Level Training in public health and a research focus on improving medical diagnosis including the development and deployment of Novel diagnostic Technologies such as

1:18:13

AI I've been a faculty member at the Johns Hopkins University School of Medicine for more than two decades where I'm currently a professor of Neurology and director of our ahrq f fed Center for Diagnostic Excellence I'm also a

1:18:25

past president of the society to improve diagnosis in medicine my testimony today will focus on opportunities and challenges for AI in health care from a public health perspective with a special emphasis on AI to improve medical

1:18:38

diagnosis I will tailor my remarks to the VA context as appropriate but I believe what I sh share here today is broadly applicable to healthc care both within and outside the VA I would like to State for the record that the

1:18:50

opinions I express here today and in my written testimony are my own and do not necessarily reflect those of the Johns Hopkins University or Johns Hopkins medicine AI is the branch of computer science concerned with endowing

1:19:02

computers with the ability to simulate intelligent human behavior the most complex cognitive task in medicine is the act of diagnosing a cause of a patient's symptoms errors in diagnosis account for an estimated 800,000 deaths or permanent disabilities

1:19:18

each year in the US including obviously our veterans more than 80% of which are associated with cognitive errors or clinical reasoning failures this creates a unique quality improvement opportunity for aib based systems to save American

1:19:33

lives at Public Health scale potential benefits of AI include Better Health outcomes for patients at lower costs greater access to and efficiency of care delivery especially for those currently underserved or disadvantaged or in rural settings and

1:19:49

decreased Healthcare Workforce burnout however none of these benefits will be realized without tackling foundational data challenges facing AI the rate limiting step for developing and implementing AI systems in healthcare is

1:20:03

no longer the technology it is the sources of data on which the technology must be trained there are multiple facets of healthcare data quality problems which I address at greater length in my written testimony however

1:20:14

in plain language they boil down to the problem of garbage in garbage out if we train AI systems on faulty data we will get faulty results AI systems that learn on faulty data will generally make the same mistakes that humans make or Worse

1:20:30

put simply if available electronic health record data sets are used to train AI systems the best we can hope for is AI systems which replicate existing safety failures or implicit human biases and the worst we can expect

1:20:45

is AI systems that are frequently wrong in their recommendations if AI based systems are deployed without adequate testing the quality of healthcare will drop not rise the VA Healthcare data environment is better suited than most to delivering

1:21:00

high quality data that might train AI systems key attributes include the va's commitment to healthcare quality and safety a large national network of providers and patients a unified health record offering greater potential for

1:21:14

standardizing data capture independence from Financial reimbursement driven problems in healthc care encounter documentation and addressing a patient population that tends to stay largely within the VA system so outcomes can be

1:21:27

better tracked over time these attributes give the vaa the opportunity to take a leading role in building highquality AI systems for AI and Healthcare to maximally benefit the health of all Americans including veterans the following are

1:21:42

essential first AI systems must be trained on gold standard data sets that are unbiased and include complete information on both clinical inputs and Care outputs two AI systems must be effectively integrated into clinical

1:21:56

workflows leveraging the strengths of computers and humans together to produce a better result than could be achieved by either alone and three wherever AI is used systems to monitor maintain and even enhance clinician skills should be

1:22:09

co-ep loyed so that clinicians and AI systems will continue to fact check each other I have three primary recommendations for the committee with regard to implementing AI at the VA with an emphasis on diagnosis first the next

1:22:23

decade must focus on constructing gold standard data sets for diagnosis the promise of AI will not be realized without quantifying bedside evaluations two AI systems must be held to a high diagnostic standard they must be

1:22:35

demonstrated scientifically to improve safety and quality over current care and then monitored closely over time and three the impact of AI on human clinical diagnostic skills must be monitored and managed clinical deployment of AI should

1:22:47

be explicitly designed to enhance rather than reduce clinician skills by applying education and human factor science thank you for this opportunity I'd be pleased to answer any questions you may have thank you very much um we're now going

1:23:00

to proceed to questions uh ranking member brownley you have five minutes uh thank you Mr Madam chair appreciate it um Mr n Jean I'm not sure that I agree with your hypothesis that if 11 yearolds uh can create AI imagine what veterans can do perhaps younger

1:23:22

veterans I would agree yes but older veterans like me I'm not so sure but hopefully we will all have our children or our grandchildren to to to help us out so um I appreciate your testimony thumbs up can I can I respond to that

1:23:39

congresswoman sure congresswoman give me 1 hour of your time and I'll prove you wrong and have you doing using and creating AI well I have heard that AI is you know going to tell you how to do it all anyway so so perhaps you're right um I

1:23:58

have to be convinced um uh Mr Rockefeller um I understand that Kura patient uh is certified through this sounds like very extensive uh process of the FED ramp um and uh even I I think Mr Worthington made comments about uh how um expansive

1:24:24

I guess and uh it it it it is and may need to be kind of looked at and evaluated from the government's perspective but tell me a little bit more about uh your uh experience becoming certified uh certainly thanks oh yeah speak into the microphone you

1:24:47

have to thanks very much the question um and and certainly there there's there's there's a lot about that um um overall um and then I'll get to a couple particular points um I think that the um and I would recommend to to to this

1:25:02

committee that the federat process maybe um uh the approvals time I think there's a fair amount of backlog in the uh in the system uh to review all these in there's several stages of review as you know um and there might I think be a

1:25:20

backlog I don't know for sure but I think that I think there might be um and so if somehow Congress could fund additional people to work on on on these uh approvals um or to uh um to focus on it more I think that'd be beneficial

1:25:37

because when we were getting it and we were very lucky right we were we were took the you know two entire years and possibly more and the reason that I mentioned this is that you know we made it through right so in fact I don't have

1:25:53

it you know I don't have a motivation to say what I'm about to say which is that I'm concerned that because the process takes um so long that the VA might be missing out on other medium or small siiz companies who want to pursue it and

1:26:12

they and they just can't last that long right they they just have to make more money on their on their own or something fortunately we we are you know well funded through our um invest and other Investments um and they all knew that

1:26:27

they were investing in us getting fed ramp which would then Le you sort of lead to other things um but I'm concerned that a lot of uh other companies might you know sort of start the process and then you know hopefully hopefully you know throw up throw up

1:26:39

their hands thank you I I don't I have just a little bit more time and I have another question so I I appreciate uh your response so um Dr Newman toker I I wanted to ask you you uh if you are aware at all of Partnerships between

1:26:56

John Hopkins and the VA that that that's going on thank you congresswoman for the question I apologize I I do not excuse me thank you congresswoman I I do not I'm not aware of those specific Partnerships to which you refer well just a a partnership around

1:27:14

AI between of University teaching hospital and VA with in terms of using AI applications um certainly as as Dr Clancy mentioned earlier uh the there's a a tight relationship between many academic medical centers and and the VA

1:27:34

system uh it happens that the affiliate in Baltimore is with the University of Maryland rather than with Johns Hopkins so some of those connections are tighter in that space I see so you talked about some of the risks and I appreciate that

1:27:47

testimony because I think we have to be uh Eyes Wide Open on that but uh knowing sort of the VA um and its operation what steps can the VA take now to avoid some of those pitfalls I I think you're taking them um in the

1:28:04

trustworthy AI framework that you have uh delineated I think three of the six pillars are absolutely crucial effective and safe fair and Equitable and accountable and monitored if those are followed the the others have some more

1:28:18

technical attributes to them but those three deal Direct ly with this issue of the safety of delivery of of the service and if they are handled well I think you will be in a good position better than I think many other places that haven't put

1:28:33

that kind of framework in place thank you happy to hear that uh yel back uh thank you very much uh it's been a very insightful testimony and as a physician and a veteran Dr Newman toer I can wholeheartedly agree and it's not

1:28:48

just what data is available to put in but what the clinician observes whatever level that clinician is U because that data whether it's verbal data whether it's observed data non-verbal communication and then actual physical

1:29:02

findings that data goes into that system which will then help with a diagnose if that data is poor or bad uh then the result will be equally bad which brings up another question and that is the VA does have an opportunity because it's a

1:29:18

relatively closed system to have a great input of data but we have Hippa regulations has there been a thought to um allowing a voluntary waiver of HIPPA for deidentified data that could go into that Matrix and be utilized to further

1:29:38

help with both machine learning and um smarter augmented intelligence uh thank you Dr Miller Meeks I I uh I'm not aware of any specific uh action that's been taken towards the IDE idea of HIPPA wavers for this specific purpose but I like the

1:29:55

thrust of your question I think it's on point there are times where uh the inability to follow a patient over time or to acquire information um prospectively in a given encounter in order to capture the sort of full diagnostic Journey For example uh may be

1:30:13

challenging because of the hippoc constraint um and I do believe that your suggestion to give patients the opportunity to assist us in in providing better care through AI is is a good one yeah and it's Imaging as as well Imaging

1:30:27

blood work um Mr Velasquez and I I can tell that you're wanting but uh in your written testimony uh you spoke to the ability of AI to help with capacity and Resource Management specifically with aligning medical staff levels optimizing

1:30:42

weight times across the direct and Community Care networks rationalizing the use of direct and Community Care and efficiently tying those options is a major concern both for Access and cost and perhaps if we can save money on one

1:30:57

or spend money wisely on another we'll have money that can go to IE I'm thinking of tech Sprints and why are we giving a million dollar prizes if we need people to uh to be able to solve a backlog on fed ramp but Mr Velasquez can

1:31:10

you talk about hey how AI would do this particularly with the decade worth of Community Care data the VA has and what some of the obstacles would be if I can weave it into the your first question around um hippo waivers and consents so

1:31:24

we spent most of my uh work for the company's work is in the private sector and so we've curated a data set of anonymized patients but they're linked so they're hashed out of about 200 million Americans and about 100 million

1:31:39

Americans emrs they're linked I don't know who they are they have a hash and it's literally I would say if you leave out Wyoming in Montana sorry Senator tester wherever you're at we pretty have healthc care view of where people live

1:31:56

and so I from a data perspective whether it's clinical capacity practice patterns uh Supply these data sets exist not just what to apply in the VA and obviously bring in the VA data sets to look at Future demands because to me it's it's

1:32:14

an issue of not so much Supply it's where's the demand and frankly where's the need and trying to predict those two using uh rules-based methodologies or regression models it's trying to predict a weather but the clouds have uh are

1:32:31

basically their behaviors their agents they change their opinions and they interact and talk among each other they emote reaction because if that's trying to manage Healthcare you think about it how the patients interact with

1:32:42

Physicians Physicians interact with each other it's a very complex dynamic system if we're going to really get our arms around understanding supply and demand Healthcare that's a a perfect use for machine learning so now I'm going to ask the

1:32:57

million-dollar question and and that is uh and it's something that uh former speaker McCarthy brought to our attention on a visit to MIT so we are members of Congress uh I have a science background as a physician but certainly

1:33:10

when it comes to technology and especially augmented artificial intelligence um our knowledge base and Foundation may be lacking but yet we are making decisions on how both fund Implement regulate uh both the promise and also the pitfalls of AI so my

1:33:34

question if you all can just briefly answer it um how would you recommend members of Congress be able to educate themselves so that we're the ranking member brownley is saying it's impossible but very very quickly what would you advise Congress

1:33:54

to do so that we can adapt you know adapt Technologies rapidly perform the proper oversight the proper protection of uh data um and to legislate in a way that's most appropriate that allows us to really effectuate the promise of AI

1:34:11

in healthcare which can be transformational take a shot this so U my company I think and I'll keep it short I focus on the use Cas because back to your point ranking the technology changes it just changed it literally moves that quick there's some

1:34:29

kids in Cal or MIT doing something that just blows us away we're not going to keep up with those and so mean we need to focus on the use case and start there or the challenge we're trying to address then back up and so having these

1:34:42

hearings have the discussions and just asking the questions what's that challenge are trying to solve and then start back up from there I think it's probably most appropriate use of uh congress's time rather than try to keep

1:34:52

up with the kids in the garage coming up with new models so Dr Newman toer and then I'll go Dr Rock Rockefeller and I think Rockefeller very briefly I think you're you're doing this by bringing in expertise I think the most important

1:35:07

piece is the diversity of that expertise uh in order to make sure that you have all the relevant perspectives on the implementation of the technology Mr Rockefeller um I would say that the first step because it's it's it's it's an accurate

1:35:28

question yet with a vague sort of response I would say the first step is to become familiar with the products and services that are being offered um by the private sector with the VA right this is what the tech Sprints enable to

1:35:42

sort of Bring It Forward to you um and and they're all sort of during that process we became very familiar with the inner workings of the VA um uh learning about the systems how to do the Integrations all of that is good

1:35:56

groundwork of knowledge to to to share with you so I would almost say the best way to break the cycle is simply look at the products and request it through whoever thank you and Mr nashan thank you Congressman just a quick thing

1:36:10

couple of things we have experience in taking people across various age groups various education profiles and converting them into patient researchers where they are applying for their own grants and getting funded we are doing

1:36:23

that with AI one of the things I would like to offer the same thing I offered uh ranking member brownley is for this entire subcommittee allow me to come and do a workshop for you give me four hours of your time and I'll have all of you

1:36:37

creating some AI or not that's useful to your lives sounds like a topic for a round table can I just clarify my impossible statement ranking member brownley would I just wanted clar ify my impossible statement I do think that members of

1:36:55

Congress most members of Congress can wrap their heads around AI applications as it relates to health care but all of the risks involved in National Security and other kinds of things and how AI is going to sort of penetrate the world I I

1:37:12

just feel as though Congress is I mean we have not figured out how to uh regulate social media and privacy issues and so forth and so on so AI is just you know way out there compared to dealing with with with Facebook so I I just

1:37:29

think that many have recommended I think have made recommendations to Congress that what the government really needs to do is provide an entirely new agency um around technology with a lot of lot of smart people within that agency that can

1:37:46

advise members of Congress uh you know how to wrestle with these uh regulations and so forth and in particularly our national security issues so thank you for letting me explain I I am now way over time ranking member brownley would you like to make

1:38:04

any closing remarks um I you know I would just like to say this is I I wish we had a you know a lot more time because I think it I'm I thank the chairwoman for uh bringing this uh forward as a topic and I think it's a really important topic that we need to

1:38:19

really focus on more um so I I I hope we will have um additional hearings as as we move forward on this and I really do think at the end of the day we should probably have a hearing with a full committee um on it as well so we can

1:38:35

really spend more time and and drilling down on it but I really do thank um all of you for being here and I am very impressed uh with your testimonies and very impressed with the work that you're doing um for the VA but of the work

1:38:51

you're doing outside of the VA um uh to to move forward with this technology so um a lot of gratitude to all of you thank you very much and again hope we will we will spend more time uh drilling down on all of this I yield

1:39:08

back well again I want to thank our panel I want to thank the VA panel appreciate all of the expertise that was here and um perhaps my comment on how we can best uh assist is my own own um my own deficits uh AI is a powerful

1:39:24

technology with great promise uh from automating tedious tasks and saving time for clinicians and administrative uh staff to aiding and diagnosis of disease and tailoring treatment AI will alter the delivery of healthc care as we've

1:39:40

heard there are concerns that must be addressed uh and uh I would like to bring representative Rosendale to my district where one of the first AI directed devices that was approved by the FDA a was uh was developed and that

1:39:54

is for diabetic retinopathy a screening tool for diabetic retinopathy and he would see the power of AI and how that's going to uh lead to uh access prevention and affordability um these concerns of course have to be addressed in how the

1:40:11

VA uses Ai and in how the VA acquires and implements AI this subcommittee will continue to exercise oversight of the VA as it moves to uh assess acquire and Implement Ai and also to educate ourselves and our members as

1:40:26

well as the public and I think continued hearings on this topic would be very beneficial if AI needs authority to do things differently this subcommittee will proactively assess the need and the impact as the VA moves forward it must

1:40:42

do so with the plan and the best interest of veterans in mind and I look forward to the pillars uh that come forward this subcommittee will do its part to ensure that those goals are met I'd like to thank all the witnesses for

1:40:54

their presence and their testimony it has been of tremendous value the complete written statements of today's witnesses will be entered into the hearing record I ask unanimous consent that all members have five legislative

1:41:05

days to revise and extend their remarks and include extraneous material hearing no objection so ordered I thank the members and the witnesses for their attendance and participation today this hearing is now adjourned thank you

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