Microsoft Research Podcast - 109 - AI, Azure and the future of healthcare with Dr. Peter Lee

Episode Date: March 4, 2020

Over the past decade, the healthcare industry has undergone a series of technological changes in an effort to modernize it and bring it into the digital world, but the call for innovation persists. One person answering that call is Dr. Peter Lee, Corporate Vice President of Microsoft Healthcare, a new organization dedicated to accelerating healthcare innovation through AI and cloud computing. Today, Dr. Lee talks about how MSR’s advances in healthcare technology are impacting the business of Microsoft Healthcare. He also explains how promising innovations like precision medicine, conversational chatbots and Azure’s API for data interoperability may make healthcare better and more efficient in the future. https://www.microsoft.com/research

In tech industry terms, you know, if the last decade was about digitizing healthcare, the next decade is about making all that digital data good for something. And that good for something is going to depend on data flowing where it needs to flow at the right time. You're listening to the Microsoft Research Podcast, a show that brings you closer to the cutting edge of technology research and the scientists behind it. I'm your host, Gretchen Huizenga. Over the past decade, the healthcare industry has undergone a series of technological changes in an effort to modernize it and bring it into the digital world.

But the call for innovation persists. One person answering that call is Dr. Peter Lee, Corporate Vice President of Microsoft Healthcare, a new organization dedicated to accelerating healthcare innovation through AI and cloud computing. Today, Dr. Lee talks about how MSR's advances in healthcare technology are impacting the business of Microsoft Healthcare.

He also explains how promising innovations like precision medicine, conversational chatbots, and Azure's API for data interoperability may make healthcare better and more efficient in the future. That and much more on this episode of the Microsoft Research Podcast. Peter Lee, welcome to the podcast. Thank you. It's great to be here. So you're a Microsoft Corporate Vice President and head of a relatively new organization here called Microsoft Healthcare. Let's start by situating that within the larger scope of Microsoft Research and Microsoft writ large. What is Microsoft Healthcare? Why was it formed? And what do you hope to do with it? It's such a great question because when we were first asked

to take this on, it was confusing to me. Healthcare is such a gigantic business in Microsoft. You know, the number that really gets me is Microsoft has commercial contracts with almost 169,000 healthcare organizations around the world. I mean, it's just massive. Basically anything from a one nurse clinic in Nairobi, Kenya to Kaiser Permanente or UnitedHealthcare and everything in between. And so it was confusing to try to understand what is Satya Nadella thinking to ask a researchy organization to take this on. But, you know, the future of healthcare is so vibrant and dynamic right now and is so dependent on AI, on cloud computing, big data. I think he was really wanting us to think about that future. Let's situate you. Okay. You cross a lot of boundaries, from pure to applied research, computer science to medicine.

You've been head of Carnegie Mellon University's computer science department, but you were also an office director at DARPA, which is the poster child for applied research. You're an ACM fellow and on the board of directors of the Allen Institute for AI, but you're also a member of the National Academy of Medicine, fairly newly minted, as I understand. Right, just this year. And on the board of Kaiser Permanente School of Medicine. So I'd ask you what gets you up in the morning, but it seems like you never go to bed. So instead, describe what you do for a living, Peter, how you choose what hat to wear in the morning and what's a typical day in your life look like? Well, you know, this was never my plan. I just love research and thinking hard about problems, being around other smart people and thinking hard about problems, getting real depth of

understanding. That's what gets me up. But I think the world today, what's so exciting about it for anyone with the research gene is that research in a variety of areas has become so important to practical everyday life. It's become important to Microsoft's business and not just Microsoft, but all of our competitors. And so I just feel like I'm in a lucky position as well as a lot of my colleagues. I don't think any of us started with that idea. We just wanted to do research and now we're finding ourselves sort of in the with that idea. We just wanted to do research, and now we're finding ourselves sort of in the middle of things. Right. Well, talk a little bit more about computer science and medicine.

How have you moved from one to the other, and how do you kind of envision yourself in this arena? Well, my joke here is these were changes that actually Satya Nadella forced me to make. And it's a little bit of a joke because I was actually honored that he would think of me this way. But it was also painful because I was in a comfort zone just doing my own research, leading research teams. And then, you know, Satya Nadella becomes the CEO. Harry Shum comes on board to drive innovation. And I get asked to think about new ways to take research ideas and get them out into the world. And then three years after that, I get asked to think about the same thing for health care.

And each one of those, to my mind, are examples of this concept that Satya likes to talk about, growth mindset. I joke that growth mindset is actually a euphemism. Because each time you're asked to make these changes, you just get this feeling of dread. You might have a minute where you're feeling honored that someone would ask you something, but then... Oh no, I got to do it now. And boy, I was on a roll in what I was doing before. And you do spend some time feeling sorry for yourself. But when you work through those moments, you find that you do have those periods in your life where you grow a lot.

And my immersion with so many great people in healthcare over the last three or four years has been one of those big growth periods. And to be recognized then, let's say, by the National Academies is sort of validation of that. All right. So rewind just a little bit and talk about that space you were in just before you got into the healthcare situation, you were doing Microsoft research. Where on the spectrum from pure, like your Carnegie Mellon roots to applied, like your DARPA roots, did that land? There's an organization called Next here, I think. Yeah. That's right. You know, when I was in academia, academia really knows how to do research. Yeah. And they really put the creatives, the graduate students and the faculty at the top of the pyramid socially in the university. It's just a great setup. And it's

organized into departments, which are each named after a research area or a discipline. And within the departments, there are groups of people organized by sub-discipline or area. And so it's an organizing principle that's tried and true. When I went to DARPA, it was completely different. The departments aren't organized by research area. They're organized by mission, some easily accessible goal or objective. You can always answer the question, have we accomplished it yet or not? And so research at DARPA is organized around those missions. And that was a big learning experience for me. It's not like saying we're going to do computer vision research. We'll be doing that for the next 50 years. It's can we eliminate the

language barrier for all internet connected people? That's a mission. You can answer the question, you know, how close are we? And so the mix between those two modes of research from academia to DARPA is something that I took with me when I joined Microsoft Research. And, you know, Microsoft Research has some mix, but I thought the balance could be slightly different. And then when Satya Nadella became the CEO and Harry Shum took over our division, they challenged me to go bigger on that idea. And that's how Next started. Next tried to organize itself by missions. And it tried to take passionate people and brilliant ideas and grow them into new lines of business, new engineering capabilities for Microsoft, and along the way, create new CVPs and TFs for our company.

There's a tension here because one of the things that's so important for great research is stability. And so when you organize things like you do in academia and in large parts of Microsoft research, you get that stability by having groups of people devoted to an area. We have, for example, say computer networking research groups that are best in the world. And they've been stable for a long time and they just create more and more knowledge and depth. And that stability is just so important. You feel like you can take big risks when you have that stability. When you are mission oriented like a NEXT, these missions are coming and going all the time.

So that has to be managed carefully. But the other benefit of that management-wise is more people get a chance to step up and express their leadership. So it's not that either model is superior to the other, but it's good to have both. And when you're in a company with all of the resources that Microsoft has, we really should have both. Well, let's zoom out and talk somewhat generally about the promise of AI, because that's where we're going to land on some of the more specific things we'll talk about in a bit. But Microsoft has several initiatives under a larger umbrella called AI for Good. And the aim is to bring the power of AI to societal scale problems in things like agriculture, broadband accessibility, education, environment, and of course, medicine. So AI for Health is one of these initiatives, but it's not the same thing as Microsoft Healthcare. Well, the whole AI for Good program is so exciting, and I'm just so proud to be in a company that makes this kind of commitment.

You can think of it as a philanthropic grants program, and it is, in fact, in all of these areas, providing funding and technical support to really worthy teams, passionate people, really trying to bring AI to bear for the greater good. But it's also the case that we devote our own research resources to these things. So it's not just giving out grants, but it's actually getting into collaborations. What's interesting about AI for Health is that it's the first pillar in the AI for Good program that actually overlaps with a business at Microsoft, and that's Microsoft Healthcare. One way I think about it is it's an outlet for researchers to think about what could AI do to advance medicine. When you talk to a lot of researchers in computer science departments

or across Microsoft research labs, increasingly you see more and more of them getting interested in healthcare and medicine. And the first things that they tend to think about if they're new to the field are diagnostic and therapeutic applications. Can we come up with something that will detect ovarian cancer earlier? Can we come up with new imaging techniques that will help radiologists do a better job. Those sorts of diagnostic therapeutic applications, I think, are incredibly important for the world, but they are not Microsoft businesses. So the AI for Health program can provide an outlet for those types of research passions. And then there are also, as a secondary element, 4 billion people on this planet today that

have no reasonable access to healthcare. AI and technology have to be part of the solution to creating that more equitable access. And so that's another element that, again, doesn't directly touch Microsoft's business today in Microsoft healthcare, but is so important. We have a lot to offer. So AI for Health is just, I think, an incredibly visionary and wonderful program for that. Well, let's zoom back out. No, let's zoom back in. I've lost track of the camera. I don't know where it is. Let's talk about the idea of precision medicine or precision healthcare

and the dream of improving those diagnostic and therapeutic interventions with AI. Tell us what precision medicine is and how that plays out. And how are the two rather culturally diverse fields of computer science and medicine coming together to solve for X here? Yeah, I think one of the things that is sometimes underappreciated is over the past 10 to 20 years, there's been a massive digitization of healthcare and medicine. After the 2008 economic collapse, in 2009, there was the ARRA. There was a piece of legislation attached to that called the HITECH Act. And HITECH actually required healthcare organizations to digitize health records. And so for the past 10 years,

we've gone from something like 15% of health records being in digital form to today, now over 98% of health records are in digital form. And along with that, medical devices that measure you have gone digital. Our ability to sequence and analyze your genome, your proteome have gone digital. And now the question is, what can we do with all the digital information? And on top of that, we have social information. People are carrying mobile devices.

People talk to computers at home. People go to their Walgreens to get their flu shots. And all of this is in digital form. And so the question is, can we take all of that digital data and use it to provide highly personalized and precisely targeted diagnostics and therapeutics to people? Can we get a holistic kind of 360 degree view of you specifically, of what's going on with you right now, and what might go on over the next several years and target your wellness. Can we advance from sick care, which is really what we have today, to healthcare? When a big tech company like Microsoft throws its hat in the healthcare ring

and publicly says that it has the goal of transforming how healthcare is experienced and delivered, I immediately think of the word disruption. But you've said healthcare isn't something you disrupt. What do you mean by that? And if disruption isn't the goal, what is? Right. You know, healthcare is not a normal business. Worldwide, it's actually a $7.5 trillion business. And for Microsoft, it's incredibly a $7.5 trillion business. And for Microsoft, it's incredibly important because, as we were discussing, it's gone digital. And increasingly, that digital data and the services and AI and computation to make good use of the data is moving to the cloud. So it has to be something that we pay very close attention to, and we have a business priority to support that. But, you know, it's not a normal business in many, many

different senses. As a patient, people don't shop, at least not on price, for their healthcare. They might go on a website to look at ratings of primary care physicians. But certainly, if you're in a car accident, you're unconscious, you're not shopping. You're just looking for the best possible care. And similarly, there is a massive shift for healthcare providers away from what's called fee-for-service and towards something called value-based care, where doctors and clinics are being reimbursed based on the quality of the outcomes. What you're trying to do is create success for those people in organizations that, let's face it, they've devoted their lives to helping people be healthier. And so it really is

almost the purest expression of Microsoft's mission of empowerment. It's not how do we create a disruption that allows us to make more money, but instead, you know, how do we empower people and organizations to deliver better and receive better health care. Today in the U.S., a primary care doctor spends almost twice as much time entering clinical documentation as they do actually taking care of patients. Some of the doctors we work with here at Microsoft call this pajama time, because you spend your day working with patients, and then at home when you crawl to bed, you have to finish up your documentation. That's a big source of burnout. And so what can we do

using speech recognition technologies, natural language processing, diarization, to enable that clinical note-taking to be dramatically reduced? You know, how would that help doctors pay more attention to their patients? There is something called revenue cycle management, and it's sort of sometimes viewed as a kind of evil way to maximize revenues in a clinic or hospital system. But it is also a place where you can really try to eliminate waste. Today in the U.S. market, most estimates say that about a trillion dollars every year is just gone to waste in the U.S. healthcare system. And so these are sort of data analysis problems in this highly complex system that really require the kind of AI and machine learning that we develop. And those are the kinds of disruptions we'd like to see.

That's right. Yeah. We'll call them successes as you did. Well, and they are disruptions, they're disruptions that help today's working doctors and nurses. They help today's hospital administrators. Let's talk about several innovations that you've actually made to help support the healthcare industry's transformation. Last year, a year ago at the HIMSS conference, you talked about tools that would improve communication, the healthcare experience, and interoperability and data sharing in the cloud. Tell us about these innovations. What did you envision then?

And now, a year later, how are they working out? Yeah, maybe the one I'd like to start with is about interoperability. I sometimes have joked that it's the least sexy topic, but it's the one that is, I think, the most important to us. In tech industry terms, you know, if the last decade was about digitizing healthcare, the next decade is about making all that digital data good for something. And that good for something is going to depend on data flowing where it needs to flow at the right time. And doing that in a way that protects people's privacy because health data is very, very personal. And so a fundamental issue there is interoperability.

Today, while we have all this digital data, it's really locked into thousands of different incompatible data formats. It doesn't get exposed through modern APIs or microservices. It's oftentimes siloed for business reasons. And so unlocking that is important. One way that we look at it here at Microsoft is we are seeing a rising tidal wave of healthcare organizations starting to move to the cloud. Probably 10 years from now, almost all healthcare organizations will be in the cloud. And so with that historic shift that will happen only once ever in human history, what can we do today to ensure that we end up in a better place 10 years from now than we are now? And interoperability is one of the keys there. And that's something that's been recognized by

multiple governments. The U.S. government, through the Centers for Medicare and Medicaid Services, has proposed new regulations that require the use of specific interoperable data standards and API frameworks. And I'm very proud that Microsoft has participated in helping endorse and guide the specific technical choices in those new rules. So what is the API that Microsoft has? So the data standard that we've put a lot of effort behind is something called FHIR, F-H-I-R, Fast Healthcare Interoperability Resources. And for anyone that's used to working on the web,

you can look at FHIR and you'll see something very familiar. It's a modern data standard. It's extensible because medical science is advancing all the time, and it's highly susceptible to analysis through machine learning. And so it's utterly modern and standardized. And I think FHIR can be a lingua franca for all healthcare data everywhere. And so for Microsoft, we've integrated FHIR as a first-class data type in our cloud in Azure. We've enabled FHIR in Office. So the Teams application, for example, can connect to health data for doctors and nurses. And there's integration going on into Dynamics. And so it's a way to convert everything that we do here at Microsoft into great healthcare-capable tools.

And once you have FHIR in the cloud, then you also suddenly unlock all of the AI tools that we have to just enable all of that precision medicine down the line. It's such a biblical reference right then, the cloud and the fire. You know, there's an endless supply of bad puns around fire, and so thank you for contributing to that. Well, it makes you think about the Fyre Festival, which was spelled F-Y-R-E, which is the biggest debacle in festival history ever. I should say, by the way, another thing that everyone connected to Microsoft should be proud of is we have really been one of the chief architects for this new future. One of the most important people in the fire development community is Josh Mandel, who works with us here at Microsoft Healthcare.

And he has the title chief architect, but it's not chief architect for Microsoft. It's chief architect for the cloud. Oh, my gosh. So he spends time talking to the folks at Google, AWS, at Salesforce, and so on, because we're trying to bring the entire cloud ecosystem along to this new future. Tell me a little bit about what role bots might play in this arena? Bots are really interesting because how many listeners have received a lab test result and have no idea what it means? How many people have received some weird piece of paper or bill in the mail from their insurance company? It's not just medical advice, where you have a scratch on your throat and you're worried about what you should do. That's important too. But the idea of bots in healthcare really span all these

other things. One of the most touching in a project led by Hadass Bittran and her team has been in the area of clinical trials. So there is a website called clinicaltrials.gov and it contains a registry describing every registered clinical trial going on. So now if you are desperate for more experimental care or you're a doctor treating someone and you're desperate for this, how do you find out of thousands of documents? And they're complicated technical medical science things. And it's difficult. If you go to clinicaltrials.gov and type into the search box breast cancer, you get hundreds of results.

So the cool project that Hadass and her team led was to use machine reading from Microsoft Research out of Hoi Fung Poon's team to read all of those clinical trials documents and create a knowledge graph

and use that knowledge graph then to drive a conversational chatbot so that you can engage in a conversation. So you can say, you know, I have breast cancer. I'm looking for a clinical trial. And the chatbot will start to ask you questions in order to narrow down eventually to the one or two or three clinical trials that might be just right for you. And so this is something that we just think has a lot of potential. And business-wise, there are more mundane but also important things.

Just call centers. Boy, those nurses are busy. What would happen if we had a bot that would triage and tee up some of those things and really give superpowers to those call center nurses. And so it's that type of thing that I think is very exciting about conversational tech in general. And of course, Microsoft Research and Next should be really proud of really pioneering a lot of this technology. So if I employed a bot to narrow down the clinical trials, could I get myself into one? Is that what you're explaining here? Yeah. In fact, the idea here is that this would help tremendously the connection

between prospective patients and clinical trials. It's so important because pharmaceutical companies and clinics that are setting up clinical trials, more than 50% of them fail to recruit enough participants. They just never get off the ground because they don't get enough. The recruitment problem is so difficult. Wow. And so this is something that can really help on both ends. I didn't even think about it from the other angle, like getting people in. I always just assume, well, a clinical trial, no biggie. It's such a sad thing that most clinical trials fail and fail because of the recruitment problem.

Huh. Well, let's talk a little bit more about some of the really interesting projects that are going on across the labs here at Microsoft Research. So what are some of the projects and who are some of the people that are working to improve health care in technology research? Yeah, I think pretty much every MSR lab is doing interesting things. There's some wonderful work going on in the Cambridge UK lab and Chris Bishop's lab there in a group being led by Aditya Nori. One of the things there has been a set of projects in collaboration with Novartis, really looking at new ideas

about AI-powered molecule design for cellular therapies, as well as very precise dosing of therapies for things like macular degeneration. And so these are sort of bringing the very best machine learning and AI researchers shoulder to shoulder with the best researchers and scientists at Novartis to really kind of innovate and invent the future. In the MSR India lab, Suram Rajamani's team, they've been standing up a really impressive set of technologies and projects that have to do with global access to healthcare. And this is something that I think is just incredibly, incredibly important. We really could enable, through more intelligent medical devices, for example, much less well-trained technicians and clinicians to be able to deliver healthcare at a distance.

The other thing that is very exciting to me there is just looking at data. You know, how do we normalize data from lots of different sources? And then MSR Asia and Beijing, they've increasingly been redirecting some of the amazing advances that that lab is famous for in computer vision to the medical imaging space. And there are just amazing possibilities in taking images that might not be high resolution enough for a precise diagnosis and using AI to kind of magically improve the resolution. And so just the cross-border, you go from kind of lab to lab,

you just see some really inspiring work going on. Yeah, some of the researchers have been on the podcast. Antonio Criminisi with InnerEye. Haven't had Ethan Jackson from Premonition yet. Oh, the Premonition. Well, Antonio Criminisi and the work that he led on InnerEye, you know, we actually went all the way to an FDA 510k approval on the tumor segmentations. And the components of that now are going into our cloud. Really amazing stuff. Yeah. And then premonition.

This is one of these things that is in the age of coronavirus. Right. I was just going to refer to that, but I thought maybe I shouldn't. The thing that is so important is, we talked of precision medicine before, but there is also an emerging science of precision population health. And in fact, the National Academy of Medicine just recently codified that as an official part of medical research. And it's bringing some of the same sort of precision medicine ideas, but to population health applications and studies. And so when you look at premonition and the ability to look at a whole community and get a genetically precise diagnosis of what is going on in that community, it is something that

could really be a game changer, especially in an era where we are seeing more challenging infectious disease outbreaks. I think a lot of people would say, can we speed that one up a little? I want you to talk for a minute about the broader tech and healthcare ecosystem and what it takes to be a leader, both thought and otherwise in the field. So you've noted that we're in the middle of a big transformation that's only going to happen once in history. And because of that, you have a question that you ask yourself and everyone who reports to you. So what's the question that you ask and how does the answer impact Microsoft's position as a leader? Right. You know, healthcare in most parts of the world is really facing some big challenges. It's at a financial breaking point in almost all developed countries. The spread of the latest access to good medical practice

has been slowing in the developing world. And as you kind of look at how to break out of these cycles, increasingly people turn to technology. And the kind of shining beacon of hope is this mountain of digital data that's being produced every single day. And so how can we convert that into what's called the triple aim of better outcomes, lower costs, and better experiences? So then when you come to Microsoft, you have to wonder, well,

if we're going to try to make a contribution, how do you do it? When Satya Nadella asked us to take this on, we told ourselves a joke that he was throwing us into the middle of the Pacific Ocean and asking us to find land. Because it's just such a big, complex space. Where do you go? And we had more jokes about this because you start swimming for a while and you start meeting lots of other people who are just as lost. And you actually feel a little ashamed to feel good about seeing other people drowning. But it fundamentally doesn't help you figure out what to work on. And so we started to ask ourselves the question, if Microsoft were to disappear today,

in what ways would healthcare be harmed or held back tomorrow and into the future? If our hyperscale cloud were to disappear today, in what ways would that matter to healthcare? If all of the AI capabilities that we can deploy so cheaply on that cloud were to disappear, how would that matter? And then since we're coming out of Microsoft Research, if Microsoft Research were to disappear today, in what ways would that matter? And asking ourselves that question has sort of helped us focus on the areas where we think we have a right to play. And I think the wonderful thing about Microsoft today is we have a business model that makes it easy to align those things to our business priorities. And so it's really a special time right now. Well, this is not to change tone really quickly, but this is the part of the podcast where I ask what could possibly go wrong. And since we've actually just used a drowning in the sea metaphor, it's probably apropos.

But when you bring nascent AI technologies, and I say nascent because most people have said, even though it's been going on for a long time, we're still in an infancy phase of these technologies. When you bring that to healthcare and you're literally dealing with life and death consequences, there's not any margin for error. So I realize that the answer to this question would be too long for the podcast, but I have to ask, what keeps you up at night? And how are you and your colleagues addressing potential negative consequences at the outset, rather than waiting for the problems to appear downstream? That's such an important question, and it actually has multiple answers. Maybe the one that I think would be most obvious to the listeners of this podcast has to do with patient safety. Medical practice and medical science has really advanced

on the idea of prospective studies and clinical validation, but that's not how computer science, broadly speaking, works. In fact, when we're talking about machine learning, it's really based on retrospective studies. We take data that was generated in the past, and we that those retrospective studies don't necessarily hold up very well prospectively. And so that gap is very dangerous. It can lead to new therapies and diagnoses that go wrong in unpredictable ways. And there is sort of an over-exuberance on both sides. As technologists, we're pretty confident about what we do, and we see lots of problems that we can solve. And the healthcare community is sometimes dazzled

by all of the magical machine learning we do. And so there can be overconfidence on both sides. That's one thing that I worry about a lot because all over our field, not just all over Microsoft, but across all the other major tech companies and universities. There are just great technologists that are doing some wonderful things and are very well-intentioned, but aren't necessarily validated in the right way.

And so that's something that really is worrisome. Going along with safety is privacy of people's health data. And while I think most people would be glad to donate their health data for scientific progress, no one wants to be exploited. Exploited for money or worse, denied, for example, insurance. And these two things can really lead to outcomes over the next decade that could really damage our ability to make good progress in the future. So that said, we're pretty good at identifying the problem. We may be able to start a good conversation, air quotes on that. But this is, for me, like, what are you doing? Because this is a huge thing. And I really think for real progress and real transformation, that the

foundations have to be right. And those foundations do start with this idea of interoperability. So the good thing is that major governments, including the U.S. government, are seeing this and are making very definitive moves to foster this interoperable future. And so now our role in that is to provide the technical guidance and the technologies so that that's done in the right way. And so everything that we at Microsoft are doing around interoperability, around security, around identity management, differential privacy,

all of the work that came out of Microsoft Research and confidential computing, all of those things are likely to be part of this future. As important as confidential computing has been as a product of Microsoft Research, it's going to be way, way more important in this healthcare future. And so it's really up to us to make sure that regulators and lawmakers and clinicians are aware and smart about these things. We can provide that technical guidance. What about the other companies that you mentioned?

I mean, you're not in this alone, and it's not just companies. It's nations and, I dare say, rogue actors that are skilled in this arena. How do you get a sort of agreement and compliance? I would say that Microsoft is in a good position because it has a clear business model. If someone is asking us, well, what are you going to do with our data? We have a very clear business model

that says that we don't monetize on your data. But everyone is going to have to figure that out. Also, when you're getting into a new area like healthcare, every tech company is a big, complicated place with lots of stakeholders, lots of competing internal interests, lots of politics. And so Microsoft, I think, is in a very good position that way, too. We're all operating as one Microsoft. But it's so important that we all find ways to work together. One point of contact has been engineered by the White House in something called the Blue Button Developers

Conference. So that's where I am literally holding hands with my counterparts at Google, at Salesforce, at Amazon, at IBM, making certain pledges there. And so the convening power of governments is pretty powerful. It's story time. We've talked a little bit about your academic and professional life. Give us a short personal history. Where did it all start for Peter Lee, and how did he end up where he is today? Oh my. It has to be short.

Well, let's see. So I'm Korean by heritage. I was born in Ohio, but Korean by heritage. And my parents immigrated from Korea. My dad was a physics professor. He's long retired now. And my mother, a chemistry professor. Wow. An Asian kid growing up in a physical science household, I was destined to become a scientist myself. And in fact, they never said it out loud, but I think it was a disappointment to them when I went to college to study math. And then maybe even a bigger disappointment when I went from math to computer science in grad school. Of course, they're very proud of me now. Where'd you go to school? I went to the University of Michigan. I was there as an undergrad, and then I was planning to go work after that.

I actually interviewed at a little tiny company in the Pacific Northwest called Microsoft. Back then. But I was wooed by my senior research advisor at Michigan to stay on for my PhD. And so I stayed and then went from grad school right to Carnegie Mellon University as a professor. And then worked your way up to leading the department. Yes, I was there for 24 years. They were wonderful years. Carnegie Mellon University is just a wonderful, wonderful place.

It's almost like there's a pipeline from Microsoft Research to Carnegie Mellon. Everyone says, CMU this, CMU that. I remember as an assistant professor when Rick Rashid came to my office to tell me that he was leaving to start this thing called Microsoft Research. And I was really sad and shocked by that. Now here I am.

Right. Well, tell us, if you can, one interesting thing about you that people might not know. I don't know if people know this or not, but I have always had an interest in cars and fast cars. I spent some time when I was young racing in something called shifter karts then later in open wheel formula ford and then when i got my first real job at carnegie mellon i had enough money that i spent quite a bit of it trying to get a sponsored ride with a semi-pro team i never managed to make it it's hard to kind of split being an assistant professor and trying to follow that passion. You know, I don't do that too much anymore. Once you are married and have a child, the annoyance factor gets a little high. But it's something that I still really love.

And there's a community of people, of course, at a place like Microsoft that's really passionate about cars as well. As we close, Peter, I'd like you to leave our listeners with some parting advice. Many of them are computer science people who may want to apply their skills in the world of healthcare, but are not sure how to get there from here. Where in the vast sea of technology and healthcare research possibilities should emerging researchers set their sights and where should they begin their swim? You know, I think it's all about data and how to make something good out of data. And today, especially, we are in that big sea of data silos. Every one of them has different formats, different rules.

Most of them don't have modern APIs. And so things that can help evolve that system to a true ocean of data, I think anything to that extent will be great. And it is not just tinkering around with interfaces. It's actually AI. To, say, normalize the schemas of two different data sets intelligently is something that we will need to do using the kind of latest machine learning, latest program synthesis, the kind of latest data science techniques that we have on offer. Who do you want on your team in the coming years? The thing that I think I find so exciting about great researchers today is their intellectual flexibility to start looking at an idea and getting more and more depth of understanding, but then evolve as a person

to understanding what is the value of this in the world and understanding that that is a competitive world. And so how willing are you to compete in that competitive marketplace to make the best stuff? And that evolution that we are seeing over and over again with people out of Microsoft Research is just incredibly exciting. When you see someone like a Galen Hunt or a Doug Berger or Lily Cheng come out of Microsoft research and then evolve into these world leaders in their respective fields, not just in research, but spanning research to really competing in a highly competitive marketplace, that is the future. Peter Lee, thank you for joining us

on the podcast today. It's been an absolute delight. Thank you for having me. It's been fun. To learn more about Dr. Peter Lee and how Microsoft is working to empower healthcare professionals around the world, visit microsoft.com slash research.