[THEME MUSIC FADES]
The book passage I read at the top is from Chapter 7, “The Ultimate Paperwork Shredder.”
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 of 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 healthcare systems are thinking about and acting on this new generation of AI is Dr. Umair Shah and Dr. Gianrico Farrugia.
Dr. Umair 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 held 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 Rickshaw Health.
Dr. Gianrico Farrugia 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 healthcare. 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. Gianrico is also a Mayo Clinic physician and professor and an author.
Umair and Gianrico are CEO-level leaders representing some of the best of the worlds of public health, healthcare delivery, medical research, and medical education.
[TRANSITION MUSIC]
Here is my interview with Dr. Umair Shah:
LEE: Umair, it’s really great to have you here.
UMAIR SHAH: Peter, it’s my pleasure. I’ve been looking forward to this conversation, and I hope you are well today.
LEE: [LAUGHS] 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.
SHAH: Sure.
LEE: 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?
SHAH: 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, [LAUGHTER] as you can imagine.
LEE: Sure.
SHAH: You know, if you like the red-blue paradigms, you couldn’t be more, you know, red and you couldn’t be more blue, I think.
LEE: Yes.
SHAH: But what happened is, back in November this past year, as I saw some of the playout of continuation of this red-blue dynamic, I made the decision to step down. And Jan. 15, I stepped down, as you mentioned, and I spent some time really thinking about what I wanted to do next and was looking at a number of opportunities.
And then a moment in time, there were some things happening in our—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.” [LAUGHTER]
And I launched Rickshaw Health (opens in new tab) and the notion that, as you know, Peter, rickshaws 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 boys to Karachi, and we were—you know, they jumped in a rickshaw and the, you know, open air [LAUGHTER] and they felt this incredible excitement.
And so Rickshaw Health was speaking to the three wheels of a rickshaw 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 healthcare environment that is constantly evolving, and we too must evolve with it. And that’s what Rickshaw 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.
LEE: 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.
SHAH: Yeah. [LAUGHS]
LEE: It’s actually … it’s actually known as quite a forward-looking place, technologically.
SHAH: That’s right.
LEE: So what was, you know, the transition like, then, going from, you know, possibly the most, sort of, maybe advanced county in the State of Texas, a large place, to the State of Washington?
SHAH: Yeah, you know, Harris County is the third-largest county in the US. So it had close to five million. And now it’s probably … it’s exceeded the five million people, and a 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 [TMC] as well, which is …
LEE: Right.
SHAH: … the largest medical center. Of course, it had to be Texas. So it can’t be the largest in the state or the country [LAUGHTER]—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, I 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, ones that do so many different things, but they’re doing them with an eye towards technology, advancements, and adoptions, 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 [lab-confirmed] death. And fast-forward a few years later, we had the fifth-lowest death rate in the US. And that was because we all came together to do so much.
LEE: Yeah, well maybe that gets us into a question that I ask 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, [start] with that job. I ask, how would you explain to your mother what you do every day?
SHAH: [LAUGHS] 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.
LEE: Yeah.
SHAH: 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 healthcare delivery ones, right.
LEE: Yup.
SHAH: 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.
LEE: Yes.
SHAH: They understood, you know, that an ambulance 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, or your brother, your neighbor, your coworker about what is public health, they have a very quizzical look on their face of what that is.
LEE: Right.
SHAH: And so what I’ve …
LEE: You know, just one thing I’ve learned is: it’s not just all the people you mentioned. Even healthcare professionals sometimes have that quizzical look.
SHAH: Yeah, good point. That’s right. Good point. And a lot of it is because we don’t get exposed to it or trained in it. You know, we think about public health when we’re 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 mosquitoes [mosquito control], and things like that. But the reality is, we do all of those things and more.
So my metaphor has been that we are the offensive line of a football team, and the healthcare delivery is the quarterback. So everybody focuses on, you know, from a few years back, everybody knows Tom Brady, right.
LEE: Yeah. [LAUGHS]
SHAH: 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 …
LEE: Right.
SHAH: … 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 healthcare delivery, the ER doc or the 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 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 and 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.
LEE: Right.
SHAH: Or you have another, you know, environmental issue that’s no longer there, you say, “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.
LEE: 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 lineman 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 touchpoints 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?
SHAH: You know, it’s interesting. Several years ago, I was in the audience with the [then] FEMA director, [Rich Serino], 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—I’m dating myself—10, 15, maybe even 20 years ago, and he said, “Everybody in the audience, there’s this, you know, app called Twitter.” And, 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 it 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 …
LEE: Right.
SHAH: … 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 NACCHO, the National Association of 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.
LEE: Hmm. Yeah. Yeah.
SHAH: 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 …
LEE: Yep. Yeah.
SHAH: … 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.
LEE: Right.
SHAH: So I have become a markedly more champion of AI. One, since I read your book. So I think there’s that. So thank you for writing it. But two, since I really recognize 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, etc.—that they are going through, and they’re doing everything in their power to get that processed and analyzed.
LEE: Yep. Yep.
SHAH: 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 …
LEE: Yep.
SHAH: … and let AI be able to do the, you know, grunt work. And similarly, as you know, measles—with the outbreaks that we’ve seen, especially in Texas but elsewhere—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.”
LEE: Hmm.
SHAH: 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, you know, our websites or come to our engagement tools—with 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.
LEE: Yeah.
SHAH: 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, …
LEE: Right.
SHAH: … “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.
LEE: I find it really interesting that you are using the terms “public health” and “population health” …
SHAH: Yeah.
LEE: … 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?
SHAH: Yeah, I think part of, and I think you and I have 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 healthcare delivery.
LEE: Yes.
SHAH: Right? That’s, it’s …
LEE: And in fact, it’s not even delivery. I think techies—I did this, too—tend to gravitate specifically to diagnosis.
SHAH: 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, it’s, I mean, there are so many use cases that are within the healthcare sector. And I think that gets back to this inequity that we have when we look at population health or, you know, this broad, um, swath of land that is, oftentimes, left behind or unexplored, and you have healthcare 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 healthcare delivery system unless we are truly looking at prevention and what’s happening in the community and the population.
LEE: Right.
SHAH: 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 systems population. It could be an insurance company saying, these are the lives covered, right. So it becomes, what is population? When we think of public health, we think of the entirety of the population, right. In the State of Washington, eight million people. Harris County, five million people. Or in the US, 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 [the] healthcare sector. And number two is, because it’s more familiar, right.
LEE: Right.
SHAH: And so there are very few practitioners similar to me that are out there, that are in the pop health who kind of know healthcare 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 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.
LEE: Yeah.
And so now I’m going to want to go into specific problems, …
SHAH: Yeah. Sure.
LEE: … 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 healthcare 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 …
SHAH: Hmm. Yeah.
LEE: … and where it needed to be.
SHAH: Yes.
LEE: And so that turned out … you would think just getting masks and gowns 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 were sitting? Because you were sitting at the helm having to deal with these problems.
SHAH: 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 or in the State of Washington, I will tell you that what I saw was that, you know, 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 … sorry, 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, it was L-E-A-H or something, or L-I or something, right. And it was just …
LEE: Right.
SHAH: … 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 you to a point of at least cleaning it up. So I don’t think it’s just that AI … AI doesn’t just save time; it saves lives. Truly used …
LEE: Hmm. Yeah.
SHAH: … that’s, I think, where we’re talking here.
And so when you have PPE and things of that nature, as you talked 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 healthcare 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 [that] 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.
LEE: Right.
SHAH: 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 this, the new agenda with Make America Healthy Again—which by the way, as you know, we had a Be Well, WA … Be Well, Washington …
LEE: Right. Yes.
SHAH: … 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.
LEE: 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 [public health agencies] in a better position to predict things now?
SHAH: 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 …
LEE: Yup. Yup.
SHAH: … where, you know, you’d have runny nose, you’d have cough, you’d have a fever, and you would take that, what was happening and people presenting to the emergency department, with what was happening in the area pharmacies where people were going to get Kleenexes and tissues …
LEE: Yep.
SHAH: … and buying over-the-counter, you know, medication, and things of that nature, Tylenol, etc.
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 many, many advancements related to wastewater surveillance over the last several years as you know …
LEE: Yep. Yep. Well, 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.
SHAH: That’s right.
LEE: They weren’t even looking for COVID. They were just taking, you know, snot samples from people.
SHAH: That’s right. That’s right. That’s right.
And so that’s the kind of thing that you, you know, we underappreciate. Is 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 healthcare provider, the doc missed it. And I wouldn’t want to say in a negative way. It was just, like, not obvious. You aren’t thinking of Ebola 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 …
LEE: Yeah.
SHAH: … 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.
LEE: Hmm. 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 PM curfew on 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?
SHAH: As you know, COVID was the most studied condition on the planet at one point. And it was, you know, things that usually we would learn 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, whether 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 policy—what are the impacts of it?
LEE: Yeah.
SHAH: 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, 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%.
LEE: Right.
SHAH: And I think, Peter, you know, the one thing we’ve got to be really mindful [of] 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.
LEE: Right.
SHAH: Now, maybe one day we can.
I’m not one of those Pollyanna-ish 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 …
LEE: Yeah. Yeah.
SHAH: … 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 that the potential is absolutely there.
LEE: So I have one more question. And 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 were significant rise of misinformation …
SHAH: Yes.
LEE: … 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?
SHAH: I think you … I don’t even have to say it … I think you hit on it, is 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, you know, 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 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, you know, it’s really up to us and this responsible 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 where we’re going to step into that are not going to be well received or successful.
You know, the one thing that’s really fascinating about this whole conversation is that this is why we’ve got to be at the table, Peter.
LEE: Yep. Yep.
SHAH: Because if we’re not at the table, you know, what’s the, you know, or 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 cannot have it on your phones. You can’t do any … ”
You know, what I really believe it does is that it takes [an] 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.
LEE: Umair, that’s really so well said, and I think a great way to wrap things up. I’ve certainly learned a lot from this conversation. So thank you again.
SHAH: 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.
[TRANSITION MUSIC]
I live in the State of Washington and because of that, I’ve been able to watch Umair in 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 a dyed-in-the-wool 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 Umair showed, it’s really important and it is arguably both an underfunded and underappreciated part of our healthcare system.
Now, public health is also very much an area that’s ripe for advancement and transformation through AI. As Umair 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. Gianrico Farrugia.
LEE: Gianrico, it’s really great to have you here today.
GIANRICO FARRUGIA: Peter, thanks for having me. Thanks for making me part of your podcast.
LEE: 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 healthcare, 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 president and CEO does, but not really. And so how would you explain to your mother what you do every day?
FARRUGIA: So, Peter, my mother’s 88 years old. She lives in Malta, and she’s visiting at the moment, …
LEE: Oh, wow.
FARRUGIA: … which is kind of nice, really.
LEE: Wow, that is amazing.
FARRUGIA: 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 the practice.
Mayo 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 a “do good to the world” standpoint.
At that point, invariably my mother will tell me that I’m working too hard. [LAUGHTER] And then of course, I change the subject, and I ask her what she cooked today because my mother, who’s 88, cooks for the whole family in 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 Mayo family.
LEE: Wow, 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.
FARRUGIA: I think I chose a little more stressful job that may limit [that]. I will tell you very briefly is that one of the AI algorithms we have estimates biological age from an electrocardiogram. My biological age jumped by 3.7 years when I became CEO.
LEE: [LAUGHS] Oh no.
FARRUGIA: I’m hoping it will reverse on the other side.
LEE: To 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 ChatGPT 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 a long time since you’ve really led Mayo Clinic to be so tech forward in this way.
FARRUGIA: 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.
LEE: Yeah.
FARRUGIA: 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—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’ve always said that it is impossible for me to understand that every single government employee is incompetent. Every physician is greedy. Something’s wrong here.
LEE: Yeah.
FARRUGIA: 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.
LEE: Yeah. When did this idea of a data platform, what you now call the Mayo Clinic Platform (opens in new tab)—by the way, I refer to this as MCP, …
FARRUGIA: Yeah, I know. [LAUGHS]
LEE: [LAUGHS] … which I always smirk a little bit because, of course, for those of us in computer science research, the AI research, MCP has also become quite a hot topic because of the model context protocol version of this. But for Mayo’s MCP, when did that become a serious, defined initiative?
FARRUGIA: 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 with a strategic plan, as I took on the job, that we needed to cure more patients. There’s just not enough cures in the world. There’s too much suffering. And that we had all these chronic diseases that people have accepted are chronic, but really the only reason that 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.
LEE: Yeah.
FARRUGIA: But I really pushed hard to say, no, it’s OK to talk about cure. It’s OK to aspire to cure. The second was connect—connecting people with data to create 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 in the singular and the plural. I’m old school. I talk about data as plural, but I know that most younger people now use data singular. [LAUGHTER] 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 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.
LEE: So I’m really wanting to delve in in this episode, in this conversation, you know, [into 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 had this thing, you know, what were the things that you were hoping to be able to show or, kind of, produce as results?
FARRUGIA: 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, did 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, OK, 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 let’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.
LEE: And so I know a very good colleague and friend of mine, John Halamka (opens in new tab), you ended up hiring. 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?
FARRUGIA: It was one of the harder decisions. First of all, [I’m] a physician myself. We tend to want to maintain some control. And so now I am the CEO, [LAUGHTER] 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 Mayo personality. And it became clear when we interviewed [that] there were some people that were really hardcore 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 interesting. We paired John with a … we created the role of a chief medical officer for the platform, which was a longstanding Mayo Clinic physician. And so we brought them together so we could get the past and the present and the future working together.
LEE: 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 were already experimenting with transformers, 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.
FARRUGIA: 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 Mayo fashion, were 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 generative 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 tool. 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, …
LEE: Yeah.
FARRUGIA: … 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 in 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 diffused 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 use. We all learned together.
LEE: Right. Yep. Yep.
FARRUGIA: But it was mostly energy, which is really interesting. It was mostly energy.
LEE: 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 on your team’s mind at all at the beginning?
FARRUGIA: So you’re asking a physician if they are skeptical about something. [LAUGHTER] Yeah. I wonder what the answer to that is. Absolutely. The first hallucination, the first wrong reference. Can you imagine if you write the grant and the wrong reference comes. As you know, …
LEE: Right.
FARRUGIA: … earlier on when some references were being made up. So massive amounts of skepticism. But the energy was such there that the people [who] were skeptical were also at the same time saying, “Let’s do a RAG [retrieval augmented generation] to clean up those references. Let’s create …” We were experimenting with discharge 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 frame. Now, I say that summarizing in hindsight.
LEE: Yeah.
FARRUGIA: 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.
LEE: Yes.
FARRUGIA: If healthcare was perfect, we would wait. Healthcare 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 were also saying the other thing, that we need to be the ones to lead validation. We need to be the ones that set the rules. We need to be participating in the creation of CHAI [Coalition for Health AI] (opens in new tab). We need to be participating as the [National] Academy of Medicine (opens in new tab).
So people did feel that Mayo was being fairly responsible about it, but that urge to, 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 well know, things evolve and change. And we’re also looking and seeing which ones work and which ones don’t and which ones we have to work together on to make better.
LEE: Yeah, you know, of course Mayo has such a, you know, such a reputation and is so influential, but in the world of healthcare broadly, 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?
FARRUGIA: 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. 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 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.
LEE: 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?
FARRUGIA: So again, I’m trying hard to try and remember how things were versus how I think about them now. [LAUGHTER] It goes back to our history. Mayo has always invested in what it thinks is the right thing that is coming. And that’s how we’ve stayed where we are. So the investment really was having an open discussion: is this worth it 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 [who] 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-range 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 the platform. And then we set up the budgets to be able to do that. And the budgets came from assets we’ve had, assets that we would get as the year came by, and then from philanthropy.
We also had a really powerful calling card. And that’s one advantage I had, and that’s … and I’d been very open when I was speaking to other CEOs that would use it is that right at that very beginning, really, really in 2019, our cardiologists, both the researchers and the clinicians, had come together and had used electrocardiograms to create an AI algorithm.
The first one was for diagnosing from an electrocardiogram, 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.
LEE: Yeah.
FARRUGIA: That quickly then came into Mayo. We put it into it so that any patient now can benefit from it. And now there are, I think, 14 algorithms just from that same one.
LEE: Yeah.
FARRUGIA: So we had a proof of concept thanks to those really far-seeing cardiologists that enabled things to happen a little faster and also, as I talked to other CEOs, enabled me to say, “This actually works. This is the path forward.” I have 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.
LEE: Wow, 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 on patient outcomes.
FARRUGIA: Yeah, they’re all linked, right.
LEE: Yes.
FARRUGIA: 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 [Clinical Detection and Response (tool)], and like most other people, I like acronyms for things. [LAUGHTER] But what it is, in our hospitals with patient consent, we monitor vitals. We monitor in the patient room—not in the ICU [intensive care unit], in the patient room. We monitor all sorts of things. But there’s a camera in 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 after you at your home. And we recently have been, looking at the last two years of data, 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 pathology’s currently run with a glass slide, not much has changed …
LEE: Yeah.
FARRUGIA: … in many, many, many years, right.
LEE: Yeah.
FARRUGIA: 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. So we worked and created a company with partners called Pramana (opens in new tab) 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 Aignostics (opens in new tab) to create a, what we call, Atlas (opens in new tab), which is an LLM that allows us to then build upon it.
LEE: Yeah.
FARRUGIA: And we, a hundred and, I think, 120 years ago, invented frozen sections at Mayo Clinic. 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, is it noninvasive cancer, or other things. So we’ve just found a way to digitize our frozen section practice and will 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 rheumatoid arthritis don’t respond to the first-line therapy, …
LEE: Right.
FARRUGIA: … 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—I don’t know how much you want to get into this, Peter, or [if] you want to talk about it yourself—MAIRA-2, which is really exciting, about how taking a simple problem—can you create a transformer that is able to detect if lines on the chest are in the right place, breathing tube is 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, …
LEE: Yeah.
FARRUGIA: … 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 exist. 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 Shah, 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. They tend to want to embrace it.
LEE: Yep.
FARRUGIA: 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.
LEE: So we’re running short on time. And so I always like to end with one or two more provocative questions. And, you know, it’s tempting 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 a 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 burdens and so on in healthcare. But you got up and you, 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, you know, a more fundamental transformation?
And so what I thought I would try to close with 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?
FARRUGIA: So first of all, darn your great memory [LAUGHTER]. Belated apologies … I probably should have …
LEE: 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.
FARRUGIA: What I was trying to get to is actually trying to clarify it in my own head and then in 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 seemed 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 it agentic AI, whatever we want to talk about. And my view was, no, is that let’s start with that aim, the last aim, and do the others because the others will come automatically if you’re working on that harder problem.
Because one, 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. 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.”
LEE: 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.
Gianrico, 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.
FARRUGIA: Thank you for making it so easy, and thanks for giving us this opportunity to do good for the world.
[TRANSITION MUSIC]
LEE: Gianrico leads what is arguably the crown jewel of the world’s healthcare systems, and so I feel it’s such a privilege to be able to talk and sometimes even brainstorm with him.
Our conversation, I think, exposed just how tech forward Gianrico is as he charts the strategies for healthcare delivery well into the 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 a 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 healthcare delivery, the connection of healthcare 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.
[THEME MUSIC]
I’d like to thank Umair 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 coauthors, Carey and Zak, will be back to examine the takeaways from our most recent conversations.
Until next time.
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