AI Proving Ground Podcast
AI deployment and adoption is complex — this podcast makes it actionable. Join top experts, IT leaders and innovators as we explore AI’s toughest challenges, uncover real-world case studies, and reveal practical insights that drive AI ROI. From strategy to execution, we break down what works (and what doesn’t) in enterprise AI. New episodes every week.
AI Proving Ground Podcast
Why AI Might Save Healthcare — And How It’s Already Happening
Can AI solve the healthcare crisis? Hear from two frontline healthcare executives — Dr. Sanaz Cordes and Dr. Eric Quinones, both Chief Digital Advisors with WWT — on the groundbreaking ways AI is reshaping clinical care, restoring physician focus, and unlocking new levels of operational efficiency. Explore how healthcare organizations are implementing AI today—with use cases including ambient scribing, inbox management, predictive analytics, and automation. Learn how CIOs and clinicians are scaling AI safely and effectively, and what it means for the future of healthcare delivery.
Whether you're a doctor, nurse, caregiver or just an individual navigating life, we are all part of the healthcare system, and AI isn't just knocking on the door. It's stepping into exam rooms, hospital corridors and administrative offices, fundamentally reshaping how care is delivered Ambient AI capturing conversations between patients and physicians, large language models assisting in the triage to chart summarization, and AI tools helping to reduce stroke risk or helping eliminate opioid use in post-op pediatric patients. This isn't just hype. This is actually happening and on today's episode, I'll be talking with a pair of chief healthcare advisors from WWT Dr Sanaz Cordes and Dr Eric Quinones. Between them, decades of experience delivering care, driving toward clinician and patient outcomes via innovative technology implementation.
Brian Feldt:But innovation alone isn't enough. This is healthcare. Trust, intention and human oversight are essential. So today, sanaz and Eric aren't just talking about what's possible. They'll be talking about what's working, what's scalable and what's next. This is the AI Proving Ground podcast from Worldwide Technology. In today's episode, we'll explore how AI is moving from concept to clinical impact, reshaping workflows, empowering clinicians and transforming the patient experience across the healthcare landscape. Dr Cortes, dr Quinones, thank you so much for joining us in the AI Proving Ground podcast today.
Sanaz Cordes, MD:Hope the two of you are doing well.
Brian Feldt:Doing great, excellent. Before we dive deep into some of the uncoverings that we found out at the HIMSS conference earlier last month, I do want to ask when it comes down to it, we are all part of the healthcare system, whether it's a clinician, whether it's a healthcare patient. I'm curious what have you seen over the last couple years in terms of how AI has altered the state of healthcare and where it's going? Eric, start with you just real quick. What have you seen from AI in the landscape?
Eric Quiñones, MD:Wow. So, going back a couple of years ago, I would say we've come a long way. Ai is not new. It's been around since the 50s. But I would say, with the promises that we'd seen maybe a decade plus ago with, you know, with Watson and things like that, I think we fell short of what the the intended promise was. I would say, even in the last couple of years, from what 2022 to maybe even last year, it was a lot of theoretical or hope what we would see. But this, this last year, I would say we're seeing a lot of true innovation when it comes down to various AI technologies being used in healthcare. So real, practical, I would say, implications that are being used today.
Brian Feldt:Yes, and as anything to add there in terms of what you've seen, or maybe even build upon what Eric just talked about and give us a little bit of what we expect over the next 6, 12, 18 months.
Sanaz Cordes, MD:Yeah, absolutely. And you know, to add on to what Eric was saying, and just even the last year, I think, with the democratization of AI, right, because now everyone's got chat GPT at home and they're using it for you know these things and they're seeing tangible outcomes from it, and it's put technology in the hands of people that have, you know, quite frankly, in the past been technology averse, right? Physicians have always had a healthy suspicion of tech and AI of all things. Is this black box, right? Like we can't see the code? We don't know that. You know, it's just very it's a different scenario than even just regular software, where they have a consensus building process of actually kind of creating the tool. So I do credit chat GPT and that whole generative AI tsunami, as Eric likes to call it, as part of kind of making it tangible in the last year or two and having so much excitement around it as well.
Sanaz Cordes, MD:The one thing I will say that this HIMSS was evident is maybe because of that and because there are so many options and shiny object syndrome, right, and CIOs who are used to the lines of business pushing back on IT. Now they're seeing those different departments coming in with their hey, can we use it for this. Hey, can we get that? That there's now a much more intentional approach to it. Where I'm hearing from leadership at HIMSS the different organizations saying we're interested in AI but know approach to it. Where I'm hearing from leadership at HIMSS, you know the different organizations saying we're interested in AI but we're going to slow it down and make sure we're very intentional about what tools we implement, how we implement them, how we measure, you know, metrics of success from them and, quite frankly, the cost right, like wanting to kind of let the noise settle and see who rises to the top, which startups or others might, you know, go off into the, into the background, so that they can really, you know, have a more intentional plan for it.
Brian Feldt:Yeah, Eric. What did you see out at HIMSS that led you to believe that we are now actually moving from that experimentation phase to the absolutely essential phase when it comes to AI in the health care setting phase?
Eric Quiñones, MD:to the absolutely essential phase when it comes to AI in the healthcare setting. Yeah, no, I think there's some really interesting things. So, you know, I think there's. When I think of the clinical workflow, where AI is really hitting the there's, you know the rubber's hitting the road would be when it comes down to ambient. You know technologies right. So being able to use those kinds of technologies to record the conversation that the physician and the patient are having in context and putting it in a clinical note, and before that patient even leaves the room, that note is ready to be reviewed by the clinician.
Eric Quiñones, MD:So there's always a person in the loop, if you will. So the clinician will be reviewing that note. Their orders may be teed up and ready to go as well. So it's really impressive to be able to see that and where they're seeing about a 70% reduction when it comes down to documentation time. That's significant. And then you know other areas that I think we're seeing it really being impactful is when it comes down to, you know, handling inbox. You know inundation that we get from various clinicians, patients et cetera, in our inboxes to be able to use. You know, large language models to help in responding to those things. And again, there's always going to be a. The physician is still going to be in the loop and going to be reviewing that. But you know the patients have been reacting very well to some of those responses because they're more detailed, uh, the more empathetic and not that we're not detailed and empathetic people, we just don't have the time sometimes to really do that.
Eric Quiñones, MD:Um, and then, lastly, I would say chart review. We spend about a third of our time in reviewing charts and patients come in time and reviewing charts. When patients come in, what are they coming in for? I forgot what's this follow-up business about, and so it will pull up all that disparate data and put it into a format where it's curated and we can see exactly what they're coming in for, what may have been missed, et cetera. So there's these real great use cases when it comes down to those kinds of things. So there's these real great use cases when it comes down to those kinds of things. I think, on the other hand, too, we're seeing it being applied when it comes down to just automation. Right, it doesn't have to be so complex, but when it comes down to automating certain processes, where we're seeing more and more clinicians being able to work at the top of their license and when I say clinicians, that's just not physicians, that's nurses as well and other folks on the care team.
Sanaz Cordes, MD:Yeah, and Eric, just to circle back to your first point about the ambient AI, Dr Poon anda few others, dr Poon from Duke, I think you were there, a bridge hosted one of the sessions and something he said really kind of resonated with me. Where, you know, we introduce a lot of technology that can improve workflows and, to some extent, obviously, the experience for clinicians. But this was the first time I heard somebody put it this way, where it's like it c ompletely changed the way doctors just like the psychology and the way they're just approaching patient care. What I mean by that is when we were in this whole, you know, two decades zone of like we're trying to talk to a patient but we're also, you know, typing everything down. We start thinking as transcriptionists and A it distracts you from the conversation. You're not as present as you need to be transcriptionist and a. It distracts you from the conversation. You're not as present as you need to be Um and B. The way you're, you know, interacting um can your goal directed for the transcription right and not necessarily the way you should be thinking as physicians. So giving that back and letting doctors just kind of go, I can like focus on this patient and really put my doctor brain the way I was trained at med school or residency to work. You know, that's just to me, you know that's an amazing thing that technology can, you know, bring back to folks.
Sanaz Cordes, MD:And then I think there was another physician, dr Mishuras, just checking it, yeah, from Mass General, and she, I think, was a part of that same panel. And when she talked about, you know, automating the clinical workflows that Eric was mentioning, I mean it's just kind of like a triple win, right, like it's really improving these clinical outcomes to levels will readily admit as a physician, sure, you know, I can probably come up with a differential diagnosis and use some apps and such, but, you know, humble enough to know that the AI could actually, you know, find a new solution, bigger and better. So you've got that, you've got the cost savings, you've got the efficiency and, of course, the patient experience. So, overall, I mean, I think it's just it's. You know, I have a lot more excitement this year because I think we're seeing actual, tangible results and it's a great time to be coming back from HIMSS riding on that high.
Brian Feldt:Yeah, love to hear it. I'm going to put a pin in that. Operational workflows here for just a second Synaz. I do want to ask you you're talking a lot about efficiencies gained through things like transcription or other use cases like that that seem pretty practical. Anything at the HIMSS conference that would speak more towards the innovation end of that spectrum that caught your eye or that you think will make a real impact either this year or in the near future?
Sanaz Cordes, MD:caught your eye or that you think will make a real impact either this year or in the near future. Yeah, absolutely. Himss really started off with kind of a bang. This year we're all kind of settling in and getting to our seats at the keynote and for me, having gone to HIMSS for over I don't know 15, 16 years now, that opening session was amazing. We had the CEO and the CMIO from Samsung Medical Center in Korea and they've won the highest level of certification for HIMSS now of any other organization, and so they presented everything that they've done at. It's like the HIMSS indicator score.
Sanaz Cordes, MD:But they presented everything they've done at this hospital and it's just amazing. They've, first of all, lessons learned. So they created a multi-stakeholder you know, multidisciplinary approach to how they're going to roll out this real connected health model, the smart hospital model, and so, for example, physicians had a seat at the table saying you know, when the EHR does X, y and Z, it's disjointed from that. We would really love it if it was this way and other folks are weighing in and they actually built their own EHR this way and it's kind of an AI first, if you will, kind of EHR model, and it integrates beyond just that right, like it's leveraging large language models to comb through data and help with decision making, but it's automating tasks downstream.
Sanaz Cordes, MD:It's doing like predictive sort of forecasting of you know, between two and six is when we have the most MRI orders or portable, you know, chest strike orders. So maybe we forecast how that's going to. We're going to tee up resource allocation and optimization, like in the future. And then, yeah, I mean it was just mind blowing because it's like if we had a blank slate and could rewrite healthcare to make it better, you know than what happened, you know, 20, 15, 20 years ago when everyone made an EHR and now we're dealing with all these siloed processes. So, and then there were the robots.
Eric Quiñones, MD:I don't know if, eric you want to talk about the robots those were. She brought robots. That was very innovative and so what it really helped do, I would say, you know, especially for the pediatric population right, that are in the hospital Again, hospitals can be very, you know, just daunting places, and even for adults, so you can imagine the children. So these robots are very interactive and they were able to, you know, with the pediatric population and be able to, you know, do things with them and, you know, I would say, somewhat distract them from all the other things that happen in a hospital. But it really helped. Calm them down is really the end point. So I thought that was a really unique way, or innovative way, to bring in some, you know, artificial intelligence into the healthcare space.
Brian Feldt:So, Naz, I am curious what took place at HIMSS in terms of the regulatory environment. I'm curious what took place at HIMSS in terms of the regulatory environment. Lots to unpack in that question and lots of news and information could happen over the next coming weeks or months, but what were physicians or clinicians talking about when it comes to the regulatory environment and how that might impact AI adoption?
Sanaz Cordes, MD:Yeah, I mean lots of changes coming for sure every hour for the next unforeseeable future, right, I mean, with political thing, you know, unprecedented changes that are being handed down and there's already a lot of impact from this. I think almost every conversation I had that factored in. I'll just give an example of, you know, academic research organizations. I worked I spoke with one of our customers that we're working with and already they are restructuring and that starts from the people, the process and the technology, right, Because with the funding change now they're having to shuffle data science dollars here and there. They're having to figure out what tech they can invest in and what's going to serve more purposes, so that they're having to be more strict about their prioritization. So it's already there and I think there's just so much unknown and that's kind of paralyzing some decision making as well. So it's already definitely having an impact for sure on the academic research organizations.
Eric Quiñones, MD:Yeah, I'll add really quick. So just I think the last week, the end of last week, so Johns Hopkins, they actually announced that they're going to be laying off to 2000 employees, including a significant amount of the researchers, because this was due to what? An $800 million grant that was cut from what? The US Agency of International Development. So you know you're going to be seeing more of that. And then also I read that because their residency programs sometimes are federally funded, you're going to be seeing these residency programs actually being cut as well, in a time where we need more residency programs. We just can't produce enough doctors. As a matter of fact, the data shows we'll never have enough doctors ever again. So you know you could have. We have a surplus of medical students, graduates, but we don't have enough residency programs to train them. So you know these kinds of things are going to impact healthcare very significantly.
Brian Feldt:I would assume Eric to just build on that. That's an opportunity as well for AI to come in and potentially supplement, or is that not necessarily the line of thinking here?
Eric Quiñones, MD:I think, maybe in some ways, but I think you can't replace some of these researchers. I mean, it's so their work that they do is so, so critical. As a matter of fact, you know, I would say that even in some of that research you know, there are, you know, maybe engineers that are AI data scientists as well. So, yeah, they're just not they're being, you know, I would say, eliminated in some ways because of not because of the it's more of the politics that's eliminating them and the grant reductions that are that are eliminating them. But, yeah, it's going to be a very interesting period, but I have faith that we're going to find a way. We're very good at doing that as humans, and I think the research is too important to just not do it, and so we're going to figure out ways to do it. It's going to be very novel, but, yeah, I don't think it'll replace the scientists in the room the scientists in the room.
Brian Feldt:Yeah, before we move on from HIMSS in general, eric, any other kind of key takeaways? I know Sanaz mentioned a couple of the keynotes, anything that you just thought would be worthwhile mentioning or things that caught your attention.
Eric Quiñones, MD:Yeah, I think, going back to real life use cases, I would say where we're seeing AI being. You know the benefits that we're seeing today. One came from Dr Chaudhry. He is the chief AI officer at Seattle Children's and there was two use cases that again, he has the right people process data technology in place and he really stresses that it's like you have to have those things to achieve your goals.
Eric Quiñones, MD:And one goal was they wanted to be able to reduce opioid treatment in terms of for the pediatric population to reduce opioids by 100%, and they use, obviously, opioids for pain and sedation, but they can have a very addictive quality to them.
Eric Quiñones, MD:So what they did was they used these various AI models to help identify different medication combinations to help achieve the goals that they were trying to achieve in terms of sedation and pain management, and they were able to do so, again by 100%. So that was significant. And then the other use case that Dr Chaudhry actually talked about was reducing pediatric strokes in post-operative patients that have had brain surgery. So when they go to the ICU, it's not uncommon for a child, to you know, to have a stroke and no matter what we do, we do the best we can, but it can happen, and you can imagine the outcomes are not very good when that happens. So they were able to apply various models to identify which particular patients are at risk and take those precautions, and so they were able to reduce those strokes by 50%. So those are significant use cases. I think that we weren't able to do before the technology existed.
Brian Feldt:Yeah, I do want to add. I want to go back to that operational workflow idea that you brought up, sanaz. I'm wondering as we integrate more AI into the workflows and the clinician setting, how is that shifting, how we work within a hospital, within a physician's office or anywhere within the healthcare setting? What are you seeing as kind of those big operational shifts as we move closer towards that AI adoption?
Sanaz Cordes, MD:Yeah, I mean, I think in many ways it's an operational shift and just a behavioral kind of again use the word psychological shift, because if you, you know, if you really think about it, the EHR and a lot of the technologies in the hospital, the EHR wasn't invented to make work better for anybody, right? It was a billing and claims repository, right, and all of a sudden, overnight, it created two hours of clicking. And you guys have all heard me say this over and over in the last decade and I really feel that just in the last couple of years. It's like doctors are taking it back, right, clinicians are taking it back. It's like, I mean, we have a shortage, right, and the numbers are all over the place, but for sure, over $60,000, 60,000 physicians shortage, you know, within the next year or two, and it's not getting any better. And so I think people finally are sitting up and sitting up straight and listening to this. So I do think it's just this shift of like we're taking back medicine, you know, and so by introducing these automations, we're just trying to almost, like, go back in time, right, we want to be able to talk to our patients and focus on what's happening, not be distracted enough to be able to take in the psychosocial things that are important, their social determinants of health, their history, and not be spending all this time.
Sanaz Cordes, MD:I mean, the rate of imaging that is redone in ERs is just staggering. Right, it's gotten better, but I remember in my days and not so long ago for others where people come in with a known underlying problem they had an MRI or whatever done in the same city maybe, and it's 11pm at night and you're not going to get that. So what do you do? You repeat the MRI, right? So I think, by automating things and letting AI and other tools and interoperability work for you to do that sort of administrative noise and let you work top of license, you know, I really feel like clinicians are taking healthcare back and again win-win for the health system, because, because MRIs cost a lot, you got to pay for all these utilizations and then that comes back. That gives back to us as a society and caring for increasing aging population, for example.
Sanaz Cordes, MD:Eric talks about that a lot and that's not going to get any better, right? So, yeah, I think it's just fundamentally going to change how we do everything. And then there's the whole operational piece, right. I mean claims and billing, and all of that has been a nightmare from day one, not just for the clinicians and nurses, but for everybody and for that department. And there's so much more they could be doing right. Think of what a disaster authorizations are for things like referrals and specialty studies and just price transparency, pathways and financial experience for patients. So if we could free those folks up to be able to work and move the needle on some of that to you know, long-windedly answer your question. I think it's just fundamentally going to shift how we do healthcare and we, quite frankly, we need it because we're at a crisis point, in my opinion, if we don't change things around.
Brian Feldt:Agreed. Yeah, not to play devil's advocate here, sinez, but what about the concept of an AI burnout loop here? If we are able to be more efficient and come to conclusions faster, does the potential exist that clinicians are just going through more and more patients Like? Is there a balance that needs to be struck in terms of giving the patients and their families or caregivers the time that they would need or deserve, as opposed to just relying on AI to get them through the door?
Sanaz Cordes, MD:Yeah, I mean, of course there's always risks with any kind of automation, I would say. My answer is I'm curious to hear Eric's is we are in such a shortage right now, especially of physicians, right where we are trying to increase other, not mid-level, but the extension practitioners, pas, nurse practitioners. We're trying to increase that population. We can't do that fast enough either. So I think that we are far far from having a problem of surplus AI and freeing up so much time that we're going to create this new swing of the pendulum. I think we're going to finally let people stop running 100 miles an hour and let them go back to a normal approach, you know, approach to a workday, which again, can only benefit everyone. So I mean, like I said, there's always a risk. I don't see that happening because we're just so far behind and there's so loss of joy in practice that I think we're going to give that back in the near term and maybe even mid to long term future, before we cross over any other threshold.
Eric Quiñones, MD:But, eric, if you want to add to that, yeah, no, I think I like throwing these stats out and they vary a little bit depending on what your resource or your resources. So in the next five years, by 2030, the numbers I've seen is we're looking at a deficit of about 140 at the most physicians across the US, about 200,000 nurses across the US in general as well, and we've been running a deficit in nursing for a while now across an average in the US. In the next five years, every baby boomer will be 65 and over. That's 20% of our population, so you're going to have an older population. That means a sicker population. In addition, in 1950, medical knowledge doubled every 50 years. Today it doubles every 74 days. I cognitively can't keep up with that. I would have to read 20 hours a day just to keep top of license, okay, so things are changing really fast.
Eric Quiñones, MD:And then the data that we have. We think an average hospital produces about 50 petabytes of data a year, but now that data is going to be going up even higher because we're going to be seeing other data sources, such as genomics, right, and the other biomes as well. We're going to be seeing other data sources, such as genomics, right and the other biomes as well. We're going to be seeing other data sources as far as wearable technologies, right, so those data sources will be coming in. So there's more data to process.
Eric Quiñones, MD:We just can't do it alone, so we need these technologies to help us curate and, really, what is the signal in the noise? Help us to be proactive and predictive, to find those patients that really need our attention well before a problem starts. So, yeah, I think we're going to be seeing a lot of change when it comes down to the way we practice medicine, and I'll even add that I know new you know residents that are new graduates in the residency programs. As they're coming out, when they're interviewing in hospitals, they're asking not about just their salaries and their other benefits. They're asking me what kind of technologies do you bring to the table that actually will help make my life different or better? So that was never a thing for me. I know that it was like go to work here and, matter of fact, it was paper which is not a bad thing, but, we.
Eric Quiñones, MD:You know we're not there anymore, but no, those are things that are happening today and, I think, where we're going to be really seeing these technologies really help and change the way we're practicing health care. I'm very hopeful and excited for the future, but right now we're still dealing with a lot of these issues that Dr Cortes has actually mentioned.
Sanaz Cordes, MD:You know, and there's also the patient piece, right, Like we're talking about operational workflows as it benefits hospitals and clinicians, but then there's the patient piece, right, so what patients or potential, you know, patients, healthcare consumers are willing to accept anymore has definitely changed, like that bar has raised. You know patients, healthcare consumers are willing to accept anymore has definitely changed, like that bar has raised. You know, and I liken it to you know, let's say you're trying to pick a restaurant to go to and you're between meetings and one has online booking for reservations and one doesn't. Which one are you going to? I mean, I'm obviously always going to default to the one that has. So patients as well.
Sanaz Cordes, MD:Right, they have choices now, and it's not just this mediocre tech health system versus you know this slightly less mediocre, it's my health system.
Sanaz Cordes, MD:Or I'm going to pay $29 or whatever and get instant access to a physician and a prescription, and you know, online, or I'm going to walk into one medical and be seen.
Sanaz Cordes, MD:So there's a lot of more.
Sanaz Cordes, MD:You know, more options.
Sanaz Cordes, MD:And so by implementing these technologies that are often, you know, dual facing right, so it allows them to be able to do dynamic appointment scheduling or medication management or, to Eric's point, as we get more data and we need to be more proactive, push more proactive reminders, you know, to the patients and just a myriad of other things that are beyond the table stakes of these types of things I'm listing right, like they want a better overall experience, access to care 24-7, have them meet them where they are right.
Sanaz Cordes, MD:Like there are some things that I'm willing to drive across town and wait an hour in a waiting room for, but there are other things that I'm frustrated that I can't just dial up, and maybe even an asynchronous visit where I don't need to have the doctor present. This should be able to be a store and forward type of engagement where somebody can deliver it. So I do think that there's that patient component and what people are now, as consumers, willing to do and they'll vote with their feet if they're not going to get it at that hospital and go somewhere else.
Brian Feldt:I think that patient experience is an interesting topic to dig into a little bit. As ambient technologies start to enter the clinician setting and you know there's kind of an always-on listening aspect to when you're in the doctor's office, does there need to be a transparency conversation with patients? And then, I guess you know, moving that conversation forward is, where does AI go from here in terms of, like, actual diagnosis?
Eric Quiñones, MD:And how do?
Sanaz Cordes, MD:health organizations balance that with what they tell the patient in the waiting room Right.
Sanaz Cordes, MD:Go ahead, eric, go ahead. I was just going to share, just to put it in kind of a perspective. You know, about 50% of patients view, you know, ai as a positive thing, right, and that's up a lot from the past. I think it's like 53 percent. So we're definitely kind of moving that needle anyway, because they again are seeing some of the benefits of that. So I just kind of wanted to make sure we touch on that, because there's there is demand and acceptance of this as a as a means to giving them the experience that they want. But yeah, eric, if you means to giving them the experience that they want. But but yeah, eric, if you wanted to go in more into what.
Eric Quiñones, MD:Yeah, no, no, that's I think. I think we're seeing very, a very positive reaction from patients. So what? The one example I mentioned earlier was inbox management, right? So let's say you know you ask a question of your physician and you're waiting for a response and something that's's before AI, there was no AI and they're just you're waiting and waiting, and waiting. You finally get a response. It could be a very curt response and you know to the point, and not that the physician was trying to be rude, it was just the time is not available to really take time to write a very lengthy, detailed response. Available to really take time to write a very lengthy, detailed response.
Eric Quiñones, MD:Fast forward where patients are getting those responses from an AI assistant and it'll say in the message this is an AI assistant for Dr So-and-so and they would get a very detailed message. They would get a very heartfelt, empathetic message and the responses that they gave when asked, when they were very, very happy with what they were receiving. So I think you know there's a lot of excitement about that, but I also think that there always has to be a person in the loop, right? This can't just be. You know this is sent out automatically when these messages. When they were sent out, they were still reviewed by the clinician and they're being sent out, but I think that's important too to know. But no, it's exciting.
Sanaz Cordes, MD:But you bring. I mean, brian, but you did bring up a good point because, like to date, I mean, I'm not aware when my doctor's using clinical decision support and using one of maybe Epic's AI algorithms to do something, and to date I think like that's okay-ish right. But you bring up a good point of at what point isn't it okay without consent right, okay without consent right. At what point, like even with Ambient, as a patient, I am really happy that I'm being recorded because, being on both sides of that equation, one of the biggest frustrations is when you go and you visit your physician and then you know you're supposed to get these follow-up things and then, as you're getting them, you're realizing wait, I already told him that I have X, y and Z, so why isn't there the prescription there?
Sanaz Cordes, MD:So, as a physician, now in that equation, you know you've got all that content and so when you're getting ready to do your, you know we call it the assessment and plan, coming up with your you know treatment plan. Like you have all that data, you're not going to miss those things. So, again, to date, like it's positive, positive, and you know we don't necessarily think about that. But you know we've got 800 FDA approved. You know AI algorithms today. What do we do, to your point, when it's like 8,000, right, there has to be some kind of process, and I don't. I don't have the answer for that, but it's really a good point and something worth watching.
Brian Feldt:Yeah, I like the idea of that, the human in the loop, that aspect, although I think it also just begs the question. I think a lot of people have this type of question, which is how effective is AI in terms of diagnosis? We recognize the need to have that human in the loop, but at what point in the future does AI or is it already becoming on par better than human diagnosis, or is it already becoming on par better than human diagnosis?
Eric Quiñones, MD:I would say it's getting there. As a matter of fact, I would say it's maybe a year ago it was at the level of a first-year resident, but now what I'm seeing is that it's surpassing that and again, what matters is the data. So we talk about, you know, interoperability is a very important part of this topic. If we don't have the data, it's hard to make those, you know, to run those algorithms appropriately to get the right outcome. But I've seen situations where they've had some really rare I would call it, you know, kind of really rare internal medicine type problems. I mean, these are real cerebral issues that a patient has. I mean, they're medical mysteries and they've applied, you know, models to. You know identify what the problem is. And differential diagnosis the first diagnosis and differential diagnosis the first diagnosis on the differential diagnosis was the correct diagnosis, so it's getting really good.
Eric Quiñones, MD:Now that doesn't mean that we just say, hey, let's go with the first diagnosis and run with that. We still have to look at that. We still have to, you know, ask the question well, what is this based on? You know, and I think when you have that transparency that's within the model and that maybe you know, dr Cordes or myself or any clinician can actually click on and see okay, this is based on this information, this, this is the you know, the evidence-based medicine it may be using, et cetera. Then it brings more credibility to what we're seeing. Nothing will take away the you know, the clinical mind. We still have to apply that.
Eric Quiñones, MD:So, but to get to the mystery quicker, to find out what's causing it quicker, I think that helps a tremendous, tremendously. You know we can spend a lot of time trying to figure out a problem in medicine and I know I've seen, you know I've seen patients go through this particular process where they've been misdiagnosed, misdiagnosed, misdiagnosed by multiple, multiple physicians. And, as a matter of fact, I have a good friend of mine, she's a nurse, a colleague. That happened to her and then I took her information, I put it in chat, gpt, just to experiment, and it actually kicked out the differential diagnosis and it was the first one that they could not identify. So it's getting pretty good.
Sanaz Cordes, MD:Wow. I didn't know that story.
Eric Quiñones, MD:That's pretty cool.
Sanaz Cordes, MD:You know, I think we can use imaging, you know, as an example, because imaging was the first right that embraced ai and radiologists embracing ai and that has come such a long way, I mean, even at worldwide. You know we've had um, we've had our data scientists just you know, on the bench who wanted to work to solve a problem, be able to, you know, do a brain tumor radiomics. You know mri project where it can diagnose the. You know the end brain tumor diagnosis like 20x faster, and that's just. You know a diagnose the. You know the end brain tumor diagnosis like 20x faster, and that's just. You know. A couple of data scientists, you know, playing on our models.
Sanaz Cordes, MD:There's solutions out there that are just they've been out there for years and so imaging, I think, is a great example of. You know it started out okay and then people were meticulous about it and now it's really ubiquitous. I mean, most health systems are using it. I do think that there's also the flip side. It's not all rosy. The EHR I mean, let's just call it what it is Epic has a habit of when these new third-party technologies become available now, most of them AI they have this habit of telling their customers oh, we're building that or it's coming in a release next year.
Sanaz Cordes, MD:Oh, don't buy this because and the health systems also don't want to be piecemealing a bunch of disparate third-party apps. So I get it, but they're also starting to get a little impatient and a little frustrated with that answer coming from Epic. So they do use obviously as much of Epic as they can, but I'm picking on Epic because I remember it wasn't that long ago where their Epic sepsis model was really faulty and you, that is not an area you want to be, you know messing with, and so we can't just kind of blindly trust it and say that it's getting as good, um, or better, globally I think it's. You know very specific um use cases that still require a lot of. You know the models getting more accurate and smarter and us holding on to that. You know cautious skepticism until you know we get to maybe the level, like, of imaging that's been there now for a while.
Brian Feldt:Well, that's a good segue into the IT conversation, which is also a very important component here, recognizing that AI is moving so incredibly fast and a CIO or an IT team for one of these healthcare organizations just have so much coming at them, whether it's a new vendor or thinking about their data estates or whatever it might be. What are some of their priorities that they must be thinking about now so that they can actually implement and have a workforce that adopts and scales AI on their behalf?
Sanaz Cordes, MD:Yeah, I mean, I'm happy to start with that. I would say, just in the last six months, that's what a lot of my meetings are about, as you know, as I meet with some of our executives at these organizations and wherever you are, however big you are, you know you could be Northwestern. Who's on the front end? I mean, they're doing amazing things and they've got a whole system and a workshop. Or you might be a smaller health system. At some point you're working towards an AI center of excellence health system. At some point you're working towards an AI center of excellence, and what I mean by that is, you know, maybe it is literally a center where you've got data scientists and folks and you know, governance committees, or maybe it's just the CIO, who's got now a hundred different use cases being demanded of him, and IT teams or, yeah, it folks that are maybe not skilled in implementing this. I mean, that's a big factor right now is just getting talent that can be skilled for AI and for the cybersecurity component around AI. But wherever you are, you're trying to work to an excellence model where there's some sort of standardization of ingesting these ideas, standardization of prioritizing and mapping them to feasibility versus cost, versus outcome and then a methodology of how they're built right. Does it come off a third party? And, if so, what are the criteria for that?
Sanaz Cordes, MD:And then Eric brought up the most important point earlier, which is the data. Dirty data in, dirty data out. We've been saying that for years about software and now more than ever with AI, we can't emphasize how important that is. So then, what's your methodology around data? And then that leads you to the whole. Like this is really kind of a platform play. These things can't happen in silos, right? Because if we can rinse and repeat to some degree all the effort we've put in to create this model off of this LLM, we need to be able to repurpose that. So it's definitely a coordinated, multidisciplinary, multi-stakeholder process and that's where we need to get. And so our goal mine, when I meet with customers is don't be overwhelmed by all that. Let's just start with one use case and figure it out. But each time we're getting smarter, just like the AI, so that as we move forward, we can go from one to five to 10 to 100 without having to, in linear form, increase the effort. You know, one to 10 to 100.
Brian Feldt:Yeah, eric, I do want to ask you about data. You've mentioned it a number of times already and, sanaz, you just mentioned it as well. Understanding that the data estate of these health care organizations is such a complex area Some may be sophisticated, some may be not as much what are we advising organizations in terms of how to best leverage their data? Is it start small and work your way up with momentum, or is it cleaning everything at once? How are we talking to clients about how they should approach and leverage their data?
Eric Quiñones, MD:Right. Right, I think one of the first things I you know well, this happens a lot when I'm visiting with clients and we're having these discussions it's do they even know where their data's at? You know it's their data governance right, starting there. And, quite frankly, a lot of them can't really answer that question, sometimes very clearly or succinctly. Or, what condition is that data right? Is it, has it been? You know, is it discrete data? You know, is it just text data? So you know, again, these are the conversations I have all the time and it's like, so that's where you kind of have to start Right. And then do you need all the data have and your AI governance?
Eric Quiñones, MD:You know folks that are in the room helping to define those particular use cases, but it really no matter all of that if you don't have the data. You know some of those core data components you can't do the. You know the work that you want to do. So really starting there is so important and really identifying where that data is going to be. So you know, maybe right now it's in a place where, like I said, it's disparate, but they're considering, you know, moving the data into an environment that will give them more accessibility to the data and who has accessibility to the data right? You know they have their own research teams that they need to have accessibility, versus others that don't need to have that accessibility. So there's a lot of questions that need to be asked and a lot of you know that come out of a lot of these meetings. And it's good because you know we do identify, you know what next steps need to happen. And so, because they do have a goal of doing a lot of these things and they know they have to do it because they cannot continue to practice medicine the way that they're doing it today they know they need to use these tools, but they can't use these tools unless they have that data governance in place. So I would even add that they need to use these tools, but they can't use these tools unless they have that data governance in place.
Sanaz Cordes, MD:So I would even add that they can't use those tools until they have the infrastructure modernized in place, right, so that's. Another thing is, like you know, when we go in there and we I mean health care, nonprofit health care is not a profitable right, like, obviously, industry there, but they're very budget restrained and so a lot of these organizations are operating on legacy tech, end of life tech, a lot of stuff's on premises, and so, again, that's why we kind of say let's just start with one use case, so that we don't overwhelm and kind of throw it all, maybe go with the bathwater, because with that one we'll figure out what's the most important. Is it your cloud migration status? Do we need to focus on a hybrid cloud model? Or is the barrier to success on that one the fact that you have a storage challenge? But there is inevitably going to be an infrastructure modernization component that goes hand-in-hand with that data strategy that Eric's referencing.
Brian Feldt:Yeah, and Eric, you mentioned data governance as well, and I think I just read maybe it was last month an ECRI report about the top 10 threats right now in healthcare data governance rated number two. And that also actually brings up another issue of just cybersecurity in general, which I understand was another important topic at HIMSS when do we stand in terms of AI, cyber data protection within the healthcare industry? What are some best practices or what are some questions that leaders need to be asking themselves?
Eric Quiñones, MD:Yeah. So, Naz, you want to. You want to take that Cause I know that you were very heavily engaged in some of those conversations.
Sanaz Cordes, MD:Yeah, yeah, I had a great session on that actually with Paolo. We hosted several folks during HIMSS. Yeah, I mean, I think AI absolutely has brought cybersecurity front and center, not that it hasn't been. I mean, we had, I think, like 720 breaches last year and sure that's down a couple percent from the year before, but we have to always think about the fact that we're like 2x where we were before 2018.
Sanaz Cordes, MD:So I mean, it's a heat and everyone knows about the change healthcare thing, and so now we've got AI and there's AI's role in cyber, got AI and you know there's AI's role in cyber. There's AI for cyber, and then there's cyber to protect against, you know, bad actors from AI, and so one of the things with why we're at such risk in healthcare is just our environment. There's a reason why you know we're in the top two costliest industries for cyber risks. Right, we've got very, very valuable data, right, patient health information, but then we've got these really unique vulnerabilities. Right. We've got all this new MIOT devices. You know all these IoT devices, smart devices that are not nearly ready and the network you know segmentation policy around them is not nearly ready, and the network you know segmentation policy around them is not nearly ready to be fully protective. We've got the very vulnerable supply chain, because it's endless and we have blind spots of you know where these things are coming from.
Sanaz Cordes, MD:And then this is something that actually I brought up in the talk that I think we don't emphasize enough. But then there's the users. We're introducing cyber tools very, very needed cyber tools to folks that literally. I mean, if you ask the average physician at an organization what's a CISO, they may not even know that answer. I said that during the session and the IT people were like but I'm telling you they don't. And and so I mean I have friends who are practicing pediatricians. They don't know what that is, so and I don't say that aren't secure. I think that's a gap and AI is just really accelerating. Our need to do that and deepfake and all these things that they brought up during the session is really scary, and so we don't want to get behind the bar on that. We got to really kind of stay forward. So there's a lot that we could be doing there.
Brian Feldt:Yeah, no, absolutely Eric. Anything to add on that, on that cyber front?
Eric Quiñones, MD:Yeah, no, I think, I think, as as we continue to move forward, I think it's a, it's a vulnerability that we, we know we're going to have and I think you know, seeing, you know, and just now has kind of mentioned it, you know, you know there's going to be this prevalence where AI is going to be involved in the surveillance of an environment, right, so you're going to have AI bots, if you will, you know, really surveying and being vigilant when it comes down to protecting a healthcare system, just as you have, as bad actors are using those same kind of bots to try to get in. So you kind of have these, you know, ai bot wars, I guess, if you can think of it that way. But I mean, that's just the reality we're living in and we're seeing right now. So it's just we cannot do it on our own. We need to have the, you know, this intelligence that's working in the background to be able to help be proactive and identify the threats this conversation a year from now.
Brian Feldt:We're out of the HIMSS conference once again. What are some of the trends and activities within healthcare related to AI, of course, that you think we'll be talking about? Is it agentic frameworks, and either physicians or patients have access to agents, that kind of work on their behalf or is it more AI diagnosis? What do you think we'll be speaking about a year from now? That'll really be important for all of the above to work towards better patient outcomes.
Eric Quiñones, MD:I'll jump in that really quick.
Eric Quiñones, MD:So my thoughts are, looking at my crystal ball, it would be we're going to have more data, right, we're going to have, you know, more data to deal with, but I think we're going to be seeing more.
Eric Quiñones, MD:Can I say it will be able to be more predictive.
Eric Quiñones, MD:Looking at populations, right.
Eric Quiñones, MD:So being able to take in social determinants of health that I mentioned before genomics data, wearable data, and being able to survey that population to be able to identify where in my population of patients are the real underserved, the marginalized, the people that we need to get activated and we need to get you know, I would say, in the system in a more proactive way. So I think we're going to see that happening more. We have to be able to scale the resources we don't have enough of. So I think we're going to see these technologies help our clinicians be able to scale by doing these kinds of things I just mentioned, to be able to watch a bigger population and to identify where those particular problems could be I shouldn't say problems, but the signal and the noise right, so you'd be able to identify yeah, this patient is going in the wrong direction. The trends are showing that and we need to get them into the doctor's office or we need to have the care team contact them Again, just having a higher touch with those patients, I think.
Brian Feldt:Yes, and as your crystal ball. What are you seeing in there?
Sanaz Cordes, MD:I think that we're going to start seeing AI be more patient facing. I think to date, a lot of it's been behind the scenes, like we talked about earlier. You know they go to do something and their app or EHR and maybe there's you know, ai happening on the background. But I think with things like pardon me virtual human or agentic AI or you know being more aware that they're interacting with AI, I think we're going to see more investment and we have had interest just in there at WWT building out solutions for that purpose, specifically like agentic AI. So I think there will be an increase in AI in the hands of patients directly.
Brian Feldt:Awesome. Well, lots happening in the space, certainly helping out in terms of clinician burnout and things of that nature. So exciting times within the healthcare industry right now as it relates to AI, and thank you to the two of you for joining us here today. It was an exciting conversation, lots of insights, so thanks for taking time out of your busy schedules.
Sanaz Cordes, MD:Thank you, Brian.
Brian Feldt:Yeah, thank you, brian, appreciate it. Okay, healthcare may be facing a crisis of capacity, but with the right application of AI, it could also be on the brink of its greatest transformation. Yet, thank you. Reflect on everything we heard today, three big lessons stand out. First, ai is no longer theoretical. Whether it's reducing documentation time through ambient listening tools or lowering pediatric stroke risk, ai is moving from pilot projects to essential tools in the care delivery process. Second, healthcare must lead with intentionality.
Brian Feldt:With so many new tools flooding the space, leaders are pushing pause, not on innovation, but on uncoordinated adoption. The name of the game is governance, infrastructure readiness and clear ROI. And third, the human element remains irreplaceable. From patient trust to clinician adoption, ai only works when people are in the loop, using these tools to augment, not replace, the irreplaceable human aspects of care. If you liked this episode of the AI Proving Ground podcast, please consider leaving a review or rating us, and sharing with friends and colleagues is always appreciated. This episode of the AI Proving Ground podcast was co-produced by Mallory Schaffran, naz Baker and Stephanie Hammond. Our audio and video engineer is John Knobloch and my name is Brian Felt. We'll see you next time.
