Microsoft Research Podcast - Reimagining healthcare delivery and public health with AI

Episode Date: August 7, 2025

Former Washington State Secretary of Health Dr. Umair Shah and Mayo Clinic CEO Dr. Gianrico Farrugia explore how healthcare leaders are approaching AI when it comes to public health, care delivery, the healthcare-research connection, and the patient experience.Show notes

In U.S. healthcare, quality ratings are increasingly used to tie the improvement in patient health outcomes to the reimbursement rates that health care providers can receive. The ability of GPT4 to understand these systems and give concrete advice has a chance to make it easier for providers to achieve success in both dimensions. This is the AI Revolution in Medicine, Brief of Zinn. Peter Lee. Shortly after OpenAI's GPD4 was publicly released, Kerry Goldberg, Dr. Zach Ohani and I published The AI Revolution in Medicine to help educate

the world of healthcare and medical research about the transformative impact this new generative AI technology can have. But because we wrote the book when GPD4 was still a secret, We had to speculate. Now, two years later, what did we get right and what did we get wrong? In this series, we'll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here. The book passage I write at the top is from Chapter 7, The Ultimate Paperwork Shreder.

Public health officials and healthcare system leaders influence the well-being and health of people at the population level. They help shape people's perceptions and responses to public health emergencies, as well as to chronic disease. They help determine the type, quality, and availability treatment. All this is critical for maintaining good public health, as well as aligning better health and financial outcomes. That, of course, is the main goal of the concept of value-based care. AI can definitely have significant ramifications for achieving this. Joining us today to talk about how leaders in public health and health care systems are

thinking about and acting on this new generation of AI is Dr. Umer Shah and Dr. Gianrico Ferrujia. Dr. Umer Shah is a nationally recognized health leader and innovator. He led one of America's top-rated pandemic responses as Washington State's Secretary of Health, a position he hailed from 2020 to 2025. Umair previously directed Harris County Public Health in Texas, overseeing large-scale emergency response for the nation's third largest county while building an emergency care career spanning 20-plus years.

He now advises organizations on health innovation and strategy as founder and principal of Rikshal Health. Dr. Jean-Rico Frugia is the president and CEO of Mayo Clinic, the world's top-ranked hospital for seven consecutive years and a pioneer in technology forward platform-based health care. Under his leadership, Mayo has built and deployed the Mayo Clinic platform. The platform enables Mayo and its partners to gain practical insights from a comprehensive repository of longitudinal, de-identified clinical data spanning four continents.

Jean Rico is also a Mayo Clinic physician and professor and an author. Umair and Jean-Rico are CEO-level leaders representing some of the best of the worlds of public health, health care delivery, medical research, and medical education. Here's my interview with Dr. Umar Shah. Umar, it's really great to have you here. Peter, it's my pleasure. I've been looking forward to this conversation, and I hope you are well today. day. I am doing extremely well. So, you know, what I'd like to do in these conversations is first just to start a little bit about you. You know, you served actually during a really tumultuous time as the Secretary of Health in the state of Washington. But you recently stepped away from that

and you started your own firm, Rickshaw Health. So can we start there? What's that all about? Yeah, no, absolutely. First of all, you know, I would say that the transition from Texas to Washington could not have been more geopolitically different, as you can imagine. Sure. You know, if you like the red-blue paradigms, you could be more, you know, red and you couldn't be more blue, I think. But what happened is back in November this past year, as I saw some of the play out of continuation of this red-blue dynamic. I made the decision to step down. And January 15th, I stepped down, as you mentioned. And I spent some time really think about what I wanted to do next

and I was looking at a number of opportunities. And then a moment in time, there was some things happening in my wife and our family's personal lives that sort of made me think that I wanted to focus a little bit more on family. And I felt the universe was saying, stay still. And I launched Rikshah Health. And the notion that, as you know, people, rickshas are oftentimes known across the globe as these modes of transport that

reliably get you through ever-changing streets and traffic patterns and all sorts of ecosystems that are evolving at all times and they get you to the other side and they get you also with a sense of exhilaration like when I took my my boys to Karachi and we were you know they jumped in a rickshaw and they you know open air and they felt this incredible excitement and so rickshaw health was speaking to the three wheels of a rickshaw that that symbolize the three children that we have and the real notion of how do we bring balance and agility and performance to the forefront and then move in an ever just like streets ever changing health care environment that is constantly evolving

and we too must evolve with it and that's what rickshah health is all about is taking clients to that next level of trying to navigate especially at this time a very very different landscape than even several months ago. So excited about it. Yeah, absolutely. You know, you made this transition from Texas to the state of Washington. And for people who listen to this podcast and don't know, the particular part of Texas, where you were Harris County, is really big, very, very important in that state. That's just not, you know, the normal county in Texas. Yeah.

It's actually known as quite a forward-looking place technologically. So what was the transition like then going from possibly the most sort of maybe advanced county in the state of Texas, a large place to the state of Washington? Yeah, you know, Harris County is the third largest county in the U.S., So it had close to 5 million, and now it's probably exceeded the 5 million people. And very diverse, very forward-looking, as you mentioned, technologically very, very much looking at what's the next horizon. And home to Texas Medical Center as well, which is the largest medical center.

Of course, it had to be Texas, so it can be the largest in the state or the country, the largest in the world, right? And TMC also had a number of different initiatives related to startups and venture capital and VC. And so they had launched something called TMCX. And that was a real opportunity. And I know you're familiar with it, an opportunity to really look at how do you incubate all sorts of different innovations and bringing private sector, public sector, as well as healthcare delivery alongside these startups to really look at the landscape. And so when I left Houston and came to Washington, I realized that obviously it was in the backyard. I mean, you know, you all at Microsoft Research and the work that you're all doing is part of an ecosystem of advanced innovation that's occurring in the Pacific Northwest.

That, you know, when we see all the players that are here, all the, you know, the ones that do so many different things, but they're doing them with an eye towards technology, advancements and adoptions, it's been, it's been quite. amazing. When I made that transition, it was really about, you know, the vaccines and what was happening with, you know, with COVID and fighting the, you know, remember, this was the state that had the first case in the continental United States, had the first outbreak and the first death. And fast forward, a few years later, we had the fifth lowest death rate in the, in the U.S. And that was because we all came together to do so much. Yeah. Well, maybe that gets us into a question that I asked a lot of our guests, which is, you know, and maybe let's, since we're on your time as the Secretary of Health in Washington State, with that job, I ask, how would you explain

to your mother what you do every day? I laugh because that's been such a fascinating conversation in public health, because we have oftentimes been, it's been really hard. to describe what that is. And, you know, there are so many metaphors and, you know, analogies that we've used. I've always wondered why we do not have more television shows or sitcoms or dramas that are about the public health workforce or the work that we do in the field. Because you have, you know, all sorts of health care delivery ones, right?

As a practicing physician for 20 years, I realized. that people knew what doctors did. They knew what nurses did, right? They intimately touch the healthcare system. They understood, you know, that an ambulance gets, picks you up at your home or somewhere else, transports you, gets you to the emergency department. The emergency department, they do some things to you or within the four walls of that ER. And then you're either admitted, sent home and several days, weeks, whatever later, you get home if you're admitted, and you start your, you know, post-hospital stay at home or your rehab or what have you. And that all is known to people.

But when you ask your mother, your grandmother, or your, you know, your uncle, your brother, your neighbor, your coworker, but what is public health? They have a very quizzical look on their face of what that is. And so what I've... You know, one thing I've learned is it's not just all the people you mentioned. health care professionals sometimes have that good good point that's right good point and a lot of it's because we don't get exposed to it or trained in you know we think about public health when we're in the in our training and you know I'm sure you had a very similar piece of this is that you know you see

it as oh that's the health department that takes care of you know STDs or it takes care you know it does the immunizations or you know maybe they do some water quality or maybe they do vector mosquitoes and things like but the reality is we do all of those things right and more and um so my my metaphor has been that we are the offensive line of a football team and the health care delivery is the quarterback so everybody focuses on you know if from a few years back everybody knows tom brady right he won the super bowls everybody knows what but if you asked people who was number 75 on the offensive line of the New England Patriots or name your favorite football team. And the answer would be you would not be able to likely answer that question. You would know Tom Brady, the quarterback, and

that's health care delivery, the ER doc or the, you know, hospitalist or the nurse or the, you know, the medical assistant or the people that are doing all the work in the field that are that are, that are the ones that are more visible. But the invisible workforce of the offensive line, that's who we don't know. And yet these are the people that are blocking. and sweating and doing all things to complement the work and make sure the quarterback is successful. And here's where the metaphor breaks down, that when Tom Brady wins the Super Bowl, we continue to invest in the offensive line because we recognize the value of it. We want the quarterback to be successful the next season.

But in public health or in society, we do the exact opposite. When tuberculosis rates come down, we say, well, you know what? we've solved the problem. We don't need it anymore. Or you have another, you know, environmental issue that's no longer there. You said, we don't need it anymore. And we disinvest from public health or that offensive line. And then you start to see those rates go back up. And so my answer to mom and grandma and dad and grandpa is we are critical to your health because we touch you every single day. And so please invest in us. Yeah. And, you know, I think I'm going to want to get a little deeper on that in just a few minutes here, because I think, especially

during the pandemic, that issue of not understanding the importance of that offensive linemen actually really came to the forefront. And so I'd like to get into that. But the kind of second kind of standard thing I've been probing with people is still just focusing on you and your background is what touch points or experiences you've had with AI in the past. And not everyone has. Like, it maybe isn't too surprising that doctors and healthcare developers, tech developers, have lots of contact with AI. But would the top dog, you know, at a public health agency ever have had significant contact with AI? What about you? you know it's interesting i i several years ago i was in the audience uh with the fema director

who just did such an incredible job and i remember he made this comment at that time and peter this may have been like i don't know fifth i'm dating myself 10 15 maybe even 20 years ago and he he said everybody in the audience uh there's this um you know um there's this uh app called uh twitter and uh you know how many people in the audience have ever sent a tweet or know about this? And I don't know, maybe it was a public health audience, maybe about 15% of the people raised their hands. He said, I challenge you to right now, pick up your phone, download the app, and go ahead and send a tweet right now.

And I remember I sent my first tweet at that time. And I was so thought-provoking for me was that he was saying you need to be engaged in social media. But the other 85% of the audience. had not even done that or had even understood the importance of social media at that time. Or maybe they understood, but they had restrictions on how to utilize, right? So that has stayed with me because that's very much about this revolution of AI, that I know that public health and population health practitioners like myself who have been in the trenches

and understand the importance of it, they really believe in the importance or think they know the importance. But NACO, the National Association and County and City Health officials had done a survey of local health agencies. And about two-thirds, if not three-quarters of local health agencies reported that they had an AI capacity that was low or lower than ideal. And that is very much where I come from. When I was in public sector and at the state health agency, our transformation was very much about how do we advance the work and how do we utilize this in a population health standpoint? And I was fortunate to have a chief of innovation at Washington State Department of Health, Les Becker, who understood the value of AI. And as you know, we did also hold a AI science convening.

that your team was there with University of Washington. And that was really an opportunity for us to say that AI is here. It's not tomorrow. It's not next year. It's not the future. It's already here. We need to embrace it. But here's the problem, Peter.

Far too few people in our field understand just how to embrace it. So I have become a markedly more champion of AI, one, since I read your book. So I think there is that. So thank you for writing it. But two, since I really recognized that when I became a solo or a primary few practitioner in my own realm, I needed to force amplify the work that I was doing. And when I look back and I continue to stay in touch with my colleagues in the field of public health, what they're also struggling with is that you have an epidemiologist who's got

a mound of information, data, statistics, et cetera, that they are going through and they're doing everything in their power to get that processed and analyzed. AI can take 80% of that and do it, and that epidemiologist can now turn to more of an overseer and a gatekeeper and to really recognize the patterns and let AI be able to do the grunt work. And similarly, as you know, measles with the outbreaks that we've seen, especially in Texas, but elsewhere, where you've got an opportunity where our communications people who are saying, look, we're about to have, or we know we're about to announce that there's a measles outbreak in, you know, in our community or our state or what have you, our region, and they can have

AI go through different press briefings and or press releases and say, give me the state of the art on how I should communicate this message to the community. And bam, you can do that, and now you can oversee that work as well. And then the third example is that we are always looking at how do we find ways to have a deeper connection with those who come to our websites or come to our engagement tools with chat bots and things like that. AI can really accelerate that work as well. So there's so many use cases that AI has for population health or public health. But I think the challenge is that we just don't have enough adoption because they're, One, we've had funding cuts, but two is that there is this real hesitation on what is it that we can do.

And I argue, the last thing I'll say about this, Peter, is that I argue that AI is happening right now. The discussions, the technology advancements, the work, the policy work, all that's happening right now. If public health practitioners are not at the table, if they're not part of the what does this look like, how does it work in our field, guess what, it's going to be done to us and for us rather than with us. And if we do not get with that and get to the table, then unfortunately it may not be exactly what we want it to be at the end of the day. I find it really interesting that you are using the terms public health and population health yeah, pretty much interchangeably here.

And I think that that's something that I think touches on an assumption that, was both implicit and explicit in the book that we wrote, which is we were making some predictions that our ability to extract insights and knowledge from population health data would be enhanced through the use of AI. And I think that it looks to me like that has been more challenging and has come along more slowly over the past two years. But what is your view? Yeah, I think part of, and I think you and I've had this conversation, you know, in bits and pieces,

I think one of the real challenges is that when even tech companies, and you can name all of them, when they look at what they're doing in the AI space, they gravitate towards health care delivery. Yes. Right? That's, it's. And in fact, it's not even delivery. I think techies, I did this too. tend to gravitate specifically to diagnosis.

Yes, that's right. That's right. You know, I think that's a really good point. And, you know, when you look at sepsis or you look at pneumonia or try to figure out ways that, you know, radiologists or X-rays or CT scans can be read. I mean, there are so many use cases that are within the health care sector. And I think that gets back to this inequity that we have when we look at population health or, you know, this broad, a swath of land that is oftentimes left behind or unexplored, and you have health care delivery. Now, healthcare delivery, we know, gets 95 cents or 96 cents of every dollar.

So it makes sense. Why? Right? But we also know that at the end of the day, we're looking at value-based outcomes. And you cannot be successful in the health care delivery system unless we are truly looking at prevention and what's happening in the community and the population. And that's why I use it interchangeably, but I know that public health has got a very specific term, and population health is a different set of ways of looking at the world. The reason that people try to shy away from pop health, in essence, is that you could talk about population health as being my population of patients in a clinic.

It could be my health system's population. It could be an insurance company saying these are the lives covered. So it becomes what is population? When we think of public health, we think of the entirety of the population, right? In state of Washington, eight million people. Harris County, five million people. Or in the U.S., you know, 300, whatever the number of millions of people that we think of the entire population. And what is it that actually impacts the health and well-being of that population is really what that's about.

Yet here's the challenge. When we then talk to those of our partners and our colleagues in the tech field, there are two things happening. One is there's a motivation because of the amount of dollars that are in health care sector. And number two is because it's more familiar, right? And so there are very few practitioners similar to me that are out there that are in the pop health who kind of know health care delivery because they've also seen patients, but they're also, they worked at that federal state, local level, community level. They've, you know, they've done, you know, various different kinds of environments.

And they say, look, I've got a perspective to really help. help this, a tech company or somebody see the rest of it. But you have to have both partners coming together to see that. And I think that's one of the real challenges that we have. Yeah. And so now I'm going to want to go into specific problems. And maybe COVID is a good thing to focus on the breadth of problems that had to get solved in pandemic response.

And where the gaps between health care delivery and public health. were really exposed. And so the first problem that I remember really keenly that just seemed so vexing was understanding where the PPE was, the personal protective equipment. Yeah. And where it needed to be.

Yes. And so that turned out, you would think just getting masks and gowns and gloves and gloves. to the right places at the right times or even understanding where they are so that you know and being able to predict you know what hospitals what clinics are most likely to get a big influx of patients during the height of the pandemic would be something that would be straightforward to solve but that turned out to be an extremely difficult problem but how did it look from where you're sitting

because you were sitting at the helm having to deal with these problems. Yeah, we were constantly chasing data and information. And oftentimes, you know, because a lot of these data systems in the public health sector have been underinvested in over the decades, then, you know, you had our biggest emergency crisis of our time. And a lot of public health agencies were either getting, you know, thrown a whole host of resources, or had to create things on the fly. And whether that was at Harris County and state of Washington, I will tell you that what I saw was that a lot of agencies across the country

were still using fax machines, you know, to get data that were coming in. And I remember actually, it's kind of a funny story. There was a fax machine that was highlighted down in our agency in Texas. And we actually had this fax machine, had mounds of, you know, data, you know, papers that were next to faxes that were coming in and all these things. And you would have, you know, Mr. Peter Lee listed as a patient. And then the next, you know, transmission would have Pete Lee. And then the next transmission would have Peter Lee, but instead of L-E-E-E-E-E-A-A-A-A-E-A-E-A-E. or something or L-I or something, right?

It was just, or you, or you had a date of birth missing or you had, you know, an address that was off. And what we realized is that over time, a lot of the data that were coming in were just incomplete data. And being able to chase that was really hard. And so, you know, I think AI has that potential to really organize it and to stratify it and to especially get, get you to a point. of at least cleaning it up.

And so I don't think it's just that AI, AI doesn't just save time. It saves lives. Truly used. That's, I think, where we're talking here. And so when you have PPE and things of that nature, as you talk about here in the state of Washington or what we were trying to do to get vaccines out or everything we're doing to try to get communication messages to the public.

And we did a fantastic job of that, although not ideal. I mean, there are so many things that I could point to that we could have done better, all of us in the field of public health and health care delivery alike, I will tell you that the one thing that stays with me is that if we had those tools then, and we had them in place then, and we had invested in them at that time in advance of, I think there was a real opportunity for us to be able to move ahead and even be better at how we affected the health outcomes of the very populations that we were trying to get to. And I think it's AI allows us to shift from reactive to proactive systems, catching health issues

before they escalate, and allow us to really communicate with empathy at scale. And when we can do those things, whether it's opioids or whether it's, you know, something that's happening related to an infectious disease or, you know, even the new agenda with Make America Healthy again, which, by the way, as you know, we had to be well wa, be well Washington very much that was about, you know, looking at, you know, physical health and nutritional health and emotional well-being and social connectedness, that there is a real opportunity for us to address the very drivers of ill health. And when we can do that and AI can help us accelerate that. I think we truly have the ability to drive down costs and increase the value

that's returned to all of us. What is your assessment of public health agencies readiness to use technology like AI? Because if there's one thing AI is good at, it's predicting things. Are they in a better position to predict things now? You know, I think it's a tale of two cities. I think on the one hand, we're better because we have the tools. On the other hand, we've lost the capacity to be able to utilize those tools. So, you know, it's a plus and a minus. Many, many years ago, there was the buzzword of what we called syndromic surveillance. And Peter, you know this term well. It was like you would have, you know, a whole host of accumulation of data points in, let's say, a hospital setting or an emergency department where, you know, you have runny nose, you'd have cough, you'd have

a fever, and you would take that what was happening in people presenting to the emergency department with what was happening in the area of pharmacies where people were going to get Kleenexes and tissues and buying over-the-counter medication and things of that, and your Tylenol, et cetera. And you would say you would put those two things together and you would come up with a quote-unquote syndrome. And you would say, our ability to say there was an alert to that syndrome allows us to say something, uh-oh, is going on in the community. And we got. got many advancements related to wastewater surveillance over the last several years. Also, wasn't patient number one in the United States discovered also because of the Seattle

flu study, or at least that sort of syndromic surveillance? They weren't even looking for COVID. They were just taking, you know, snot samples from people. That's right. And so that's the kind of thing that we underappreciate is you have to, you have to have a smart, intelligent, agile practitioner, right? So if I think about down in Dallas when Ebola was, you know, the gentleman who was, you know, the index case for Ebola was sent out of the emergency department and came back several days later. And it was the nurse who

picked up this time because the practitioner, the provider, the health care provider, the doc missed it. And I wouldn't want to say in a negative way was just like not obvious. You aren't thinking of a bowl in the middle of Texas. And it was the nurse who picked up. There's something wrong here. And what AI has the ability to do is to pick up those symptoms or those patterns and be able to recognize the importance of those and be able to then alert the practitioner. So what I, we call it artificial intelligence. It almost becomes artificial wisdom. Yeah, interesting.

So that actually reminds me of my next question, which is another thing that I watched you and public health officials do is try to play what-if games. So, for example, I think one decision you were involved in had to do with, you know, what would be the impact if we put a ban on large gatherings like concerts or movie theaters or imposed an 8 p.m. curfew? restaurants and you were trying to play what-if games like what would be the impact on the spread of the pandemic there so now again today with AI would that aspect of what you did play out differently than it did during the pandemic as you

know COVID was the most studied condition on the planet at one point and it was it is, you know, things that were, usually we would learn in over years or months. We were learning in weeks or days or hours. And I remember in Houston, I would say something in the morning. And I would always try to give the caveat, this is the best information we know right now because it kept changing, whether it was around masks or whether it was around, you know, the way the virus was operating, and it was around. I remember even I was just watching something recently where I was asked to comment

about whether spiders could transmit COVID-19. you know, just questions that were just evolving, evolving, evolving, and the information was evolving by morning, you would say something, by evening it would change. And why I say that is that it would have been great in the pandemic if we could have said, if you could give us all the information that's happening across the globe, synthesize that information, and be able to help us forecast the right decisions that we should be making and help us model that information.

So we could decide if you did a curfew or if you did, you know, a mask, or if you could, you know, change something else related to a policy, what are the impacts of it? What we found constantly in public health was that we were weighing decisions in incomplete data, incomplete information. So great now that everybody can armchair quarterback looking back three to five years ago and say, I would have done it this way or I would have done it that way. Gosh, I would have as well. But guess what? We didn't have that information at that time. And so you had to make the best decisions you could with incomplete data, but what AI has the potential to do is to help complete the incomplete data. Now, it's not going

to get 100%. And I think, Peter, you know, the one thing we've got to be really mindful is phantom information or information where it sort of makes up things or may somehow get you incomplete information or skews it a certain way. This is why we can't take the person out of it yet. Now, maybe one day we can. I'm not, I'm not one of those polyanish that people will never be replaced. I actually believe that those people who are skilled with AI and the tools will eventually have a competitive advantage over those who are not, just like if I had a physician who knows how to use their smartphone or knows how to use a word processor or knows how to do a PowerPoint presentation is going to replace the ones that use scantrons or the ones that write it on pieces

of paper that eventually it makes it more efficient and effective but we're not there yet but i think the potential is absolutely there so i have one more question then you can kind of tell i'm trying to expand people's understanding of just the incredible breadth of what goes on in public health, you know, all of these sorts of different issues. And again, just sticking to COVID, but this is a much broader issue. Another thing you had to cope with, where significant rise of misinformation. Yes. And maybe going along with that, very, very significant inequities in outcomes in the COVID response. And when you think about AI there, I think you can argue it both ways, that it both exacerbates

the problem, but also gives you new tools to mitigate the problems. What is your view? I think you hit, I don't even have to say it. I think you hit on it. It's that, you know, it really is two sides of one coin. On the one hand, it has the power of really advancing and allowing us to move forward in a way that incredibly accelerates and accentuates, but on the other hand, in the case of inequities, right? So if you have inequitable information data that's already out in the literature,

or already out in the media or what have you about a certain population or people or certain kinds of ideas or thoughts, etc., then AI will tend to accumulate that. You're going to take that information thinking that's the best out there, but it may have missed out on information. and now you go with it. And that's a potential problem. And I think it's the same thing on information, and is that when we have people that are able to classify or misclassify information, I think it really becomes hard because it can accelerate the inequities of trust

or inequities of trusted sources of information. It can also close the gap. So I think it, you know, it's really up to us in this response. AI to really think about how we can go about doing this in a way that's going to allow us to further the advancements, but also be careful of those, you know, those kind of places we're going to step into that are not going to be well received or successful. You know, the one thing that's, that's really fascinating about this whole conversation is that this is why we've got to be at the table, Peter. Yeah. Because if we're not at the table, you know, what's the, you know, or if

If tech companies that are out there doing this work and aren't even seeing a field of practitioners that are actually wrestling with the same problems but just cannot actually get to the solutions, we're just going to continue to accentuate the problems. And that's why I'm a firm proponent of we've got to be at the table. And so even when we've seen in, and this is going to be a little controversial, but governmental spaces where, you know, policymakers, have said, look, we are not going to let you do certain things. Or they say to public health practitioners or even healthcare delivery practitioners in certain spaces, you cannot even play

with this. You can not have it on your phones. You can't do any. You know, what I really believe it does is that it takes a almost like we put our head in the sand type of approach, rather than saying what is it that we can do to help improve AI and make it work for all of us, what we're doing is we're essentially saying we're going to let the tech companies and all the other developers come up with the solutions, but it's not going to be informed by the people in the field. And that's dangerous. We have to do both. We have to be working together. Umair, that's really so well said. And I think a great way to wrap things up. I certainly learned a lot from this conversation. So thank you again. It's been a pleasure to be with you this morning. Thank you so much for the time. And I'm looking forward to further conversations.

I live in the state of Washington. And because of it, I've been able to watch Umer and, action as our state's former Secretary of Health. And some of that action was pretty intense, to say the least, because his tenure as Secretary of Health spanned the period of the COVID pandemic. Now, as it died in the world techie, I have to admit that at the beginning, I don't think I really understood the scope and importance of the field of public health. But as the conversation with Umerer showed, it's really important and it is our. arguably both an underfunded and underappreciated part of our healthcare system. Now, public health is also very much an area that's right for advancement and transformation

through AI. As Umeir explained in our discussion, the core of public health is the idea of population health, the idea of extracting new health insights from signals from population scale data. And already we're starting to see AI making a difference. Now, here's my interview with Dr. Jean-Rico Faruja. Jean-Rico, it's really great to have you here today. Peter, thanks for having me. Thanks for making me part of your podcast.

You know, what I'd like to do in these conversations is, you know, we'll definitely want to talk about the overall healthcare system, the state of health care and what AI could or, might do to help or even hurt all of that. But I always like to start with a sharper focus just on you specifically. And my first question always is, you know, I think people imagine what a hospital or a health system present and CEO does, but not really.

And so how would you explain to your mother what you do every day? So Peter, my mother's 88 years old. She lives in Malta, and she's visiting at the moment, which is kind of great. Wow. Wow, that is amazing. I'm proud that she's still proud of me. So she does ask, I'll tell her the scope of Mayo Clinic. We serve patients across the globe. We have about 83,000 staff members that work with us, and we're very proud of the work we do in research, education, and practice. Mero Clinic is built to serve people with serious disease. So, we're very proud of the work we do in research, education and we're very proud of the work. Mero Clinic is built to serve people with serious disease. So, What I tell my mother is that here we are, we're a healthcare organization that knows what it needs to do, keep patients as the North Star, the needs of the patient come first. We have 83,000 people who want to do that, several thousand physicians and scientists. My job is to look slightly ahead and then share what I'm seeing and then sort of smooth the way for others to make sure Mayo remains true to its mission, but also true to the fact that at the moment we are in a category of one, we need to remain there, not just from an ego standpoint, but really from too good to the world standpoint. At that point, invariably, my mother will tell me that I'm working too hard. And then, of course, I changed the subject, and I ask her what she cooked today, because my mother is 88, cooks for the whole family of Malta, and there are usually four generations eating around the table.

So I tell her what she does for the family is what I do for the male family. Oh, that's a great way to put it. And it sounds like you actually have a good chance to have some good genes if she's still that active at age 88. I think I chose a little more stressful job that may limit. I will tell you very briefly is that one of the AI algorithms we have estimates It's a biological age from an ectocardogram. My biological age jumped by 3.7 years when I became CEO. I'm hoping it were reverse on the other side.

Just stick with you just for one more moment here. Second question I ask is about your origin story with respect to AI. And typically, for most people, there is AI before chat GPT and generative AI. and then after the generative AI revolution. So can you share a little bit about this? Because it must be the case that you've been thinking about this long time since you've really led Mayo Clinic to be so tucked forward in this way. Well, I've been, as you said, a physician for way too long.

I got my MD degree in 87, so that sort of dates me. But it also means that I saw a lot of the promise for AI that never seemed to pan out for decades and decades and decades like you did. Around 10 years ago, Mayo could sense that there was something different, that something was changing, that we actually, at that time, predictive AI, could make a big difference. And I think that's the moment where I and others jumped in and said, Mayo Clinic needs to be involved. And then about six years ago, when I was six and a half years ago, I became CEO, it was clear that there was the right confluence of data, knowledge, tech expertise, that we could deal with what was increasingly bothering me, which is that we knew what was coming from a technology standpoint.

And we knew the current healthcare system could not deliver on what patients need and want within that current system. And so the answer is how could a place like Mayo Clinic with our reputation not jump in and say there has to be a better way of doing things? I always said that it is impossible for me to understand that every single government employee is incompetent. Every physician is greedy. Something is wrong here. And that wrong was the architecture was wrong. And we knew that we could incorporate AI and make it better.

So for me, that journey was one of wait, wait, wait, 10 years ago, begin to jump in. six years ago, really jump in with our platform, and then, of course, in November 2022, things changed again. Yeah. When did this idea of a data platform, what you now call the Mayo Clinic platform? By the way, I refer to this as MCP, which I always smirk a little bit, because of course, for those of us in computer science research and the AI research, MCP has also become quite a hot topic. and because of the model context protocol version of this. But for Mayo's MCP, when did that become a serious defined initiative? So around the end of 18, 2018, beginning of 2019, at that point, we knew that we were going to do something differently.

We came up to strategic plan, as I took on the job, that we needed to cure more patients. They're just not enough cures in the world. There's too much suffering. And that we had all these chronic diseases that people have accepted our chronic, but really the only reason a disease is chronic is you haven't cured it. And physicians have been afraid to talk about cure, because of course, eventually everybody passes away. But I really pushed hard to say, no, it's okay to talk about cure.

It's okay to aspire to cure. The second was connect, connecting people with data to great new knowledge. and that's where it became clear that data were not currently in a format that were particularly useful. By the way, you'll hear me talk about data on the singular and the prural. I'm old school. I talk about data as prural, but I know that most younger people now use data as singular, and I apologize if I'll go through that. And then the third was transform.

Let's use Mayo's resources to transform healthcare for ourselves and for others. And that's the concept of if we are able to use. use data in a different way. Let's create a different architecture. And that architecture had to be very closely linked to using artificial intelligence in order to create better outcomes for patients. So patients can live not only longer lives, but healthier lives. And that's the genesis of MCP, Mayo Clinic platform. So I'll timestamp that as end of 2018, beginning of 2019. So I'm really wanting to delve in in this episode and in this conversation, you know, the mindset of a health system or hospital CEO. And so you're obviously thinking about, I guess, machine learning and predictive analytics and so on.

what were the kind of like in 2018 what were the outcomes that you were dreaming about from this so if you have this thing you know what were the things that you were hoping to be able to show or or kind of produce as results so first of all I think all of us who work at Mayo Clinic and this tends to be a bit sugary but it's true strongly feel that we have a responsibility to leave the place better than when we started. And so the Mayo Brothers, when they started, the two really important things. The first was that they created the first integrated healthcare system. And the second, they created the first unified record.

And that record was, of course, paper at that point. Part of that is to say, okay, what does it look like now versus how can we improve what we have if it'd be blasphemy to say, let's think of ourselves. as the Mayo Brothers, but let's think of ourselves as reasonably smart people at Mayo Clinic, really lucky to be surrounded by very smart people with resources. What will we do? And so we said, let's not aim for the low-hanging fruit. Let's aim to get at whatever you want to call it, the intractable knot, the hardest problem. And that is clinical care. Let's improve clinical care. Yes, we can deal with burnout. Yes, we can deal with administrative.

burden, but that's not focus on that. Let's really create an architecture that allows us to tackle better clinical outcomes. And by starting there, then everything flows from that. That it's not really worth doing unless at the end of the day, people are experiencing better health. And so I know a very good colleague and friend of mine, John Halamka, you ended up higher I thought he was a very interesting choice because he is, of course, in terms of technology, quite deep and very expert, but he's, I think, first and foremost, a doctor.

And so I assume you must have had to decide what type of person you would bring in and what kinds of people you would bring in to try to create such a thing. What was your thinking around the choice of someone like John? That was one of the harder decisions First of all, physician myself, we tend to want to maintain some control.

So now I am the CEO and I have to give this baby to somebody else. That's very hard. Second is Mayo Clinic is really good because it is flat. And we run a lot by committee. But it also means that therefore you have to work really hard at change and you cannot change by fiat. You have to change by convincing people. So I just, I've always made the point that the right change agent is a servant leader because that's how change becomes embedded. But it also means you've got to have that personality, the male personality. And it became clear

when we interviewed, there were some people that were really hard or tech, others that were passionate about social issues. But John really fit. that of being, as you said, deep in IT, but also himself very aligned with the Mayo Clinic values. It's as if he was a Mayo Clinic physician, even though he wasn't. And that came together, and I felt, we felt that, as we were hiring, that we could do it. And then we did something interestingly. We paired John with a, we created the role of a chief medical officer for the platform, which was a long-standing Mayo Clinic physician.

And so we brought them together so we could get the past and the present and the future working together. So I'm going to ask you about what has come out of this. But before that, let's get back to this origin story. So now all of that is being set up starting around 2018. But then, you know, in 2022, there is generative AI. Now you are already experimenting with Transformers and starting with Bert out of Google. there. So maybe that's a couple of years earlier. But still, there has to come a point where things are feeling very disrupted. Yeah. So, you know, it really wasn't. It to me was a relief

because it gave this, we were feeling pretty good about what we're doing. We were feeling a little impatient, but in true may or fashion, we're willing to sort of do everything. take its time, take it to the right committees, get the right approvals, and get it done. And so when Geront of AI came for us, it's like, I wouldn't say we told you so, but it's like, ah, there you go, here's another two. This is what we've been talking about. Now we can do it even better. Now we can move even faster. Now we can do more for our patients. It truly never was disruptive. It truly immediately became enabling, which is strange, right? Because something as disruptive as that instantly became enabling at Mayo Clinic.

And I'll take, as I think about it with you and take a moment to think and reflect on it, I think there were a couple of decisions we made earlier on that really helped us. We made the decision against the advice of any consulting firm to completely decentralize AI at Mayo Clinic six years ago. And we told our clinical department, you need to own this. You need to hire basic scientists and AI. We'll help you by creating the infrastructure. We'll help you by doing all the rest.

We'll have the compute. We'll have the partners. You need to do this on your own. You need to treat this the same way as if a new radiological technique happened or a new surgical technique happened. And so there was a lot of expertise already present in a very diffuse way that then we were able to layer on

generative AI onto that. and we found a very willingness to embrace it. In fact, I would argue initially a bit too willing because, as you know, we haven't quite figured out what's legitimate use, what's not used. We all learned together. But it was mostly energy, which is really interesting. It was mostly energy.

Wow. And, you know, it's an amazing thing to hear because one common theme that we hear is that the initial reaction is, oftentimes one of skepticism. In fact, I've been very open that even I initially had some skepticism. Was that not present in your mind or in your team's mind at all at the beginning? So you're asking a physician if they are skeptical about subject. Yeah. I wonder what the answer to that is. We absolutely, the first hallucination, the first wrong reference, can you imagine

if you write the grant and the wrong reference comes, as you know, earlier on, when some Some references were being made up. So massive amounts of skepticism, but the energy was such there that the people were skeptical, were also at the same time saying, let's do a rag to clean up those references. Let's create, we were experimenting with this chart summaries, but let's use AI to police AI and let's see what's going on. So there was more massive skepticism, but the energy was pushing that skepticism into a positive versus into a negative phrase. Now, I say that summarizing in hindsight.

Day to day, much more complicated than that. But overall, if you just, and remember, I had been at the World Economic Forum many years ago and had said healthcare needs to run towards AI. If healthcare was perfect, we would wait. health care is not perfect by any means, therefore let's run and embrace AI. And sort of that mentality was part of who we were, because at the same time, we are also saying the other thing,

that we need to be ones to lead validation. We need to be ones that set the rules. We need to be participating in the creation of chai. We need to be participating National Academy of Medicine. So people did feel that Mayo was being fairly responsible about it, but that urge the needs of the patient come first, was the driver that kept people wanting to say, not ready yet, but let's make it ready.

And we now have 320 algorithms in the practice. And they run and we constantly are looking and seeing what else we can do to improve. But as you will know, things evolve and change. And we're also looking at seeing which ones work and which ones don't and which ones we have to work together on to make better. Yeah. You know, of course, Mayo has such a, you know, such a reputation and is so influential,

but in the world of health care part, let's just focus on the United States to start. How common is this experience, you know, so if you are at a meeting with fellow CEOs of hospitals and health systems, what is the attitude and what is the kind of, how common is the approach to all of this? I think it's more common now, but going back a few years, I think it's fair to say that it was scary for people to know how it's going to change things. Healthcare runs on very narrow margins. It's very expensive.

And so your expenses and your revenue are both massive and they are very close to each other. So anything that changes, that balance is really scary because it's not. like you have the opportunity to erode into a margin or get it right the second time. So I think that is what drove a lot of what drove a lot of the initial hesitancy was one is lack of knowledge and two, understanding that you didn't have a lot of room to make a mistake. On the on the economics of this, when you are embarking on what I suspect is a very expensive initiative like Mayo Clinic platform, how on earth do you justify that early on? So, again, I'm trying hard to try and remember how things were versus how I think about them now.

It goes back to our history. Mayo has always invested in what it thinks is the right thing that is coming. That's how we've stayed where we are. So the investment really was having an open discussion is disworthed for our patients. And once that discussion was over, then the board was saying, go, go, go. Now, we are lucky in that we have the size that we're able to hire and absorb. We're lucky in that the people came before us have been financially astute and one of our values is stewardship. And we're lucky that we had a lot of patients at Mayo Clinic who were able to listen, be inspired by and be willing to help support. And so that gave us the ability to build what we're doing,

not only into the long-wage plan, but actually into the yearly plan. And so we built it into the yearly plan. We set up a center for digital health. We set up at the platform. And then we set up the budgets to be able to do that. And the budgets came from assets we've had, that we would get as the air came by and then from philanthropy. We also had a really powerful calling card, and that's one advantage I had, and I've been very

open when I was speaking to other CEOs that I would use it, is that right at that very beginning, really, really in 2019, our cardiologists, both the researchers and the clinicians that come together. and had used electrocardiograms to create an AI algorithm. The first one was for diagnosing from an electrocardi, which is very cheap, very easy to do, left ventricular dysfunction. That's how hard the left part of the heart contracts.

If it doesn't do well, you get heart failure. And they were able to show that that algorithm was already making nurses better than the physician without the algorithm. and after that went on to show that you could do it from a single strip, really with an area under the curve for that single strip on a watch that was as good as mammograms or pap smears. And so we already had that proof. That quickly then came into Mayo.

We put it into it so that any patients now can benefit from it. And now there are, I think, 14 algorithms just from that same one. So we had a proof of concept thanks to those really far-seeing card. biologists that enabled things to happen a little faster. And also, as I talk to other CEOs, enabled me to say, this actually works. This is the path forward. I've recently been vocal about also saying we are at a point now where I believe that for some medical conditions, it is not right to not use AI to help treat them.

Oh, that's so interesting. So I think I want to get into another topic here, which is when you think about the use of AI and data, what are some of the results that maybe are top of mind for you or you think are particularly important? And if you don't mind, I'd like to see if we can think about this, not only in terms of results in terms of patient outcomes, but in your other activities, core activities, like research in the education mission, and then even in the broader impacts on the healthcare system. But maybe we start with patient outcomes.

Yeah, they're all linked, right? They're part of the same ecosystem. We think of ourselves as three shields, research education and the practice, and that one goes into the other. So, as I said, we have about 320 AI algorithms from the practice. some run on every patient, some run on some patients, and we have good evidence for what they do. So some specific examples, and then I'll get into the transformer part of this. We have a program called Cedar, and like most other people, like acronyms for things,

but what it is in our hospitals with patient consent, we monitor vitals, we monitor, in the patient room, not in the ICU, in the patient room, we monitor all sorts of things, but there's a camera on the room, and we have a team of intensivists, nurses and physicians, who do not have any patient responsibilities, but are just monitoring the algorithms. And when the algorithms are predicting decompensation, they're able to get into the room. And what we've shown, for example, with that algorithm, is we've shown we've decreased length of stay in the hospital, decreased transfers into the intensive care units, and interestingly, decreased mortality and morbidity, which is not easy to show.

I talked about the electrocardiogram as a good example. Of course, everybody knows about the radiology things. We've created, taken part of this and said, if we can do this in the hospital, why cannot we do it in patients' homes? So being very active in looking after patients that would come to the ED emergency room would normally be admitted and we say, no, here are the things we can give you, go home if you want to, and we will safely look off at your home. And we recently have been looking at the last two years of data, I've been able to show that we're also successfully able to give intravenous chemotherapy in patients' homes because we can monitor, we can do all the things. that we can do. Now, with generative AI, that gave us many other opportunities. One biggest opportunity for me has always been digital pathology. When we see how pathologies currently run with a glass light, not much has changed in many, many, many years, right? And so really, we have made a

massive push to digitize pathology, not just for us, but for others, but talking about ourselves, we started by saying, it has to be very cheap to digitize it. It worked and created the company with partners called Pramana that allows us to digitize slides relatively cheaply using AI algorithms that can take away the dirt, the fingerprint. And so we end up with 21 million of our slides digitized, and that gives you now a massive opportunity. Worked with another company called agnostics to create a what we call Atlas, which is an LLM that allows us to then build upon it.

And we, 100, I think 120 years ago, invented frozen sections at Mayo Clay. So what that is, is that while the patient's still on the table, you can take a piece of tissue, look at it, and tell the surgeon the margins of what you're trying to resect are clear or not. But as a result of that, because you have to hurry, you get no information as a surgeon about is it an invasive cancer, non-invasive cancer, other things? So we've just found a way to digitize our frozen section practice and we'll completely go across the enterprise with AI-enabled digitized frozen sections,

which then enables us to then do it for anybody across the globe if we need to. And then in the genomic space, we're working to create a true exomic transformer that is short range. and we originally started doing it to see if we can test it against the fact that 40% of people with remote ad arthritis don't respond to the first line therapy

but you have to wait six months to find out and we found that we can actually do that but it has much greater uses of course and then we're working with you and I don't know how much you want to get into this Peter or you want to talk about it yourself Myra too which is really exciting about how taking a simple problem

can you create a transformer that is able to detective lines on the chest are in the right place, breathing tubes in the right place, and then do it in a way that then can be used for many, many other things. And then, Peter, because you asked about education and research. Imagine what this does now to the education system, right? And so we've got to train our physicians differently. We now have an AI curriculum for all our medical students. We offer masters and PhDs in AI. we think it's essential for the people who want to be able to truly become experts, the same way I became an expert in my area of research. And then from a research standpoint, when you think about all the registries that exist in people's labs, all the spatial genomics, all the epigenomics, all the omics that exists, and if you are able to coalesce them into one big, what we call an Atlas,

how that could really spur research at a scale that we haven't thought of before. And so that is our aim at the moment. From a research standpoint, we are with Vijay Shao, who's our dean of research, is to say, let's make the effort of making sure all the data are available to be able to use and enable for us to take advantage of AI. And that is not easy because, of course, people have collected the data. that they tend to want to embrace it. So there have to be the right incentives, the right privacy,

and the right ways of doing it. And we think we're on the way there, and we're already seeing some advantages from doing it this way. So we're running short of time. And so I always like to end with one or two more provocative questions. And, you know, it's attempting to ask you the provocative question of whether you think AI will ever replace human doctors. But I don't want to go there with you.

In fact, as I thought about our discussion, I was reflecting, we were at a conference together once and I was on stage in the fireside chat. And then, you know, after the fireside chat, there were audience questions. And I don't remember any of the questions from the audience except yours. And just to remind you, you know, I think when I was on stage, we were talking about a lot of practical uses of AI to, let's say, reduce administrative birth. and so on in healthcare. But you got up and you've,

I won't say you scolded me, but you more or less said, is it the right idea to use AI to optimize today's somewhat broken healthcare system or should we be thinking more boldly about a more fundamental transformation? And so what I thought I would try to close with here here is to hear what was really behind that question you know what were you trying to get me to

think about when you asked that question first of all darn your your your great memory belated apologies it was by far the best and most sophisticated and i think thought-provoking question of all of the ones that came out of the audience well i was trying to get to is actually trying to clarify it in my own head and then on the head of others is that we do not need to have a linear path to get to where we want to get to. And we seem to be on a linear path, which is let's try and reduce administrative burden. Let's try and truly be a companion to a physician or other provider. Let's make their problems better, make them feel better about providing healthcare and then in the next step we keep going until we get to

now we can call agency care whatever one to talk about and my view was no is that let's start with that aim the last day and do the others because the others will come automatically if you're working on that harder problem because one you to get to that harder problem you'll find all the other solutions. I was just trying to push that here's this wonderful tool that's been given to us. Let's take advantage of it as quickly as we can. And we got, I think we had gotten a little too sensitized to need to say the right things. Careful, be very careful, versus saying massive opportunity, do it right, and healthcare will be much better. Go for it. Well, I think I understand better now where the vision, insight, and frankly, courage to take on something as

ambitious and transformational as the Mayo Clinic platform, and really all of your leadership in your tenure as the president and CEO of Mayo Clinic. I think I understand it much better now. Jean-RICO, it's just always such a privilege to interact with you and now to have a chance to work with you more closely. So thank you for everything that you do, and thank you for joining us today. Thank you for making it so easy, and thanks for giving us this opportunity to do good for the world.

John Rico leads what is arguably the crown jewel of the world's health care systems. And so I feel it's such a privilege to be able to talk and sometimes even brainstorm with them. Our conversation, I think, exposed just how tech forward Gianrico is as he charts the strategies for health care delivery well into the future. future. And as I've interacted with many others, what I've learned is that this is a common trait among major health system CEOs. Roughly speaking, like we've seen in previous episodes where doctors and med students are polymath clinician technologists, the same thing is true of health system CEOs and other leaders. AI in the mind of health system CEO today is not only a technology that can transform diagnosis and treatment, but it's also something that can

have a huge impact on the business of health care delivery, the connection of health care to medical research, and the journeys that patients go through as they seek better health. These two conversations show that virtually all leaders in health and medicine are confronting head-on the opportunities, challenges, and the reality of AI, and they see a future that is potentially very different than what we have today. I'd like to thank Umeir and Gianrico again for their time and insights. And to our listeners, thank you for joining us. We hope you'll tune in to our final episode of the series.

My co-authors, Carrie and Zach, will be back to examine the takeaways from our most recent conversations. Until next time.