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January 7, 2022: John Halamka, MD, President of the Mayo Clinic Platform is our Keynote guest today. What's the promise of AI in healthcare? Why are we pursuing it? What are the challenges that remain in its adoption? Where does the technology stand in terms of transparency and useful testing ability? What are the three main problems with EHR data sets and how can we address them? How far away are we from solid clinical use cases with wrist wearables? Are we getting to real quality data that can be used? And can we call the EHR a platform? Or is it still a transactional system?

Key Points:

00:00:00 - Intro

00:06:00 - AI has a credibility problem in healthcare

00:07:30 - ​​Algorithms are mostly probabilistic, multi-tiered mathematical equations that don't necessarily have easy explainability

00:19:00 - Is the EHR a platform? Or is it more like a transactional system with aspects of a platform?

00:20:10 - The perfect storm for innovation is an alignment of technology, policy and culture. 

00:31:35 - You can augment humans to make them wildly more productive if they’re doing review rather than authorship


The Credibility of AI, the Future of the EHR, and the Cultural Demands with John Halamka

Episode 476: Transcript - January 7, 2022

This transcription is provided by artificial intelligence. We believe in technology but understand that even the smartest robots can sometimes get speech recognition wrong.

Bill Russell: [00:00:00] Today on This Week in Health IT.

John Halamka: AI has a credibility problem in healthcare. And what I mean by that is, if you buy a can of soup, on it, it says a thousand milligrams of sodium. 500 grams of fat. 2000 calories a serving. My bet is you wouldn't eat that soup. One hopes. You buy an AI algorithm and there's no soup flavor. Right. You have no idea if it was developed on people like the patient in [00:00:30] front of you or not. And therefore you really don't understand its utility, its bias, its likelihood fulfilling what you need it to do.

Bill Russell: Thanks for joining us on this week health Keynote. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of This Week in Health IT. A channel dedicated to keeping health it staff current and engaged. Special thanks to our Keynote show sponsors Sirius [00:01:00]Healthcare, VMware, Transcarent, Press Ganey, Semperis and Veritas for choosing to invest in developing the next generation of health IT leaders.

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Today we have Dr. John Halamka, President of the Mayo Clinic Platform with us. John, welcome back to the show.

John Halamka: Well, hey, thanks so much. When people ask me, what's the weather in Boston today? And I said, snowy, with a chance of Omicron.

Bill Russell: Oh man. So are you guys [00:02:30] seeing the numbers go up in Massachusetts?

John Halamka: So of course this is complicated and it's quite early, so one doesn't want to draw conclusions, but what I know about vaccine durability, Which is that the Madonna, Johnson and Pfizer vaccines have a durability of between 120 and 150 days. Now, when I say durability, I'm not referring to their prevention of serious disease or hospitalization, I'm saying durability against simple infection. [00:03:00] So just think for a moment, if in fact it was eight months ago that you got your last shot, you know, by now you're starting to see the waning of the protective antibodies and therefore it is likely you're going to see an infections spike as folks are that tweener period between second and third shot. So Omicron again it's just too early to know.

Bill Russell: I went to the, I went to the HLTH conference. The HLTH [00:03:30] conference up in Boston. I don't know if you were at that conference, we didn't run into each other.

John Halamka: Indeed I was. So you remember the best part of that conference is not the speakers. It's all of the events around the conference.

Bill Russell: Yeah the conversations that you can have. I did find the event their precautionary measures, it was really well thought out. I mean, we're trying to figure out how to come back together. We were all tested in Massachusetts. It was indoor mask mandate. And you know, you felt pretty, pretty safe. I felt at that conference with how they handled everything. But you're right it's [00:04:00]generally the hallway conversations I did happen to catch Jonathan Bush and Glen Tullman. I just went to that for entertainment and it was entertaining having them talk about their first time on stage together.

And also just talking about the EHR market. And later on in the show, I wouldn't mind talking to you about the EHR market cause you're one of the few people that is actually written code. Coded an EHR. I think what I want to do is talk to you about the EHR as a platform. [00:04:30] But I don't really want to start there.

I want to start with all right. So you're president of the Mayo platform. We've already covered that on the show. If people are wondering, Hey, what's John doing these days they can listen to the other show. You could give a brief thing on that. Are you still practicing medicine or just predominantly the Mayo platform at this point?

John Halamka: So you may know that those of us who are an academic healthcare often use this term, the triple threat. Now, what does that mean? We don't really say that at Mayo. That was more of a Harvard thing. It means you [00:05:00] practice, you teach and you do research and therefore you're everything in one package. So, I have continued over the last 40 years to see patients.

I see about 900 a year. I do toxicology consults across the country. I happen to have a particular expertise in poisonous mushrooms and plants. And so I am doing that virtually, which really was much easier during a COVID waiver and regulatory roll back here. And then I teach about 200 hours a year and I collate a [00:05:30] number of research projects in addition to the administrative duties. So welcome to life as an academic physician.

Bill Russell: You get to do it all. We are going to talk a fair amount about AI. You spoke at HIMSS last year. And you talked about the promise is bright for AI. And you talked about that there's challenges that remain for the adoption of AI. Let's start with that. Give us a little background on some of the things you talked about at the HIMSS conference.

John Halamka: Well, let me [00:06:00] break it into a couple of chunks. So the first is AI has a credibility problem in healthcare. And what I mean by that is, and you've probably heard me say this before. If you buy a can of soup on it, it says a thousand milligrams of sodium 500 grams of fat, 2000 calories, a serving.

My bet is you wouldn't eat that soup. One hopes. You buy an AI algorithm and there's [00:06:30] no soup flavor. Right. You have no idea if it was developed on people like the patient in front of you or not. And therefore you really don't understand its utility, its bias, its likelihood fulfilling what you need it to do.

So that's part of the credibility problem. So here's an interesting question is we should address AI transparency so that when we have an algorithm, you say, oh, well, this was developed only on north American [00:07:00] people who go to academic medical centers, therefore using it in Sweden may not be so good.

But you need more than that. And that second thing, what I would say we need is test ability. So the first thing is transparency. How was it developed and where is it useful testability. So you know what, Hey Bill, I'm gonna run this algorithm against and you know when it says you're likely to wear a red shirt today.

Oh, Hey, that seems like it's a pretty reasonable algorithm. It seemed to be sort of fit for purpose. And [00:07:30] building a national and international framework for testability that enables any dataset and any algorithm to come together and for evaluation is, is a challenge. So got to work on that. And then explainability, there's a lot of debate about this.

So, you know that algorithms are mostly probabilistic, multi-tiered mathematical equations that don't necessarily have easy explainability. They're a black box. [00:08:00] So, as I debate with those in the industry, they say be transparent and be testable. Explainability, we may not get to.

Bill Russell: All right. So let's break that down. Well, let's start with this. Who's developing the algorithms today. Is it predominantly academic medical centers? Is it third parties? Is it big tech? I mean who is developing these clinical algorithms within our AI models?

John Halamka: Yes. And what [00:08:30] I mean by that is right. So you're seeing some academic health centers create these algorithms. They may be based on their own internal data for their own internal use. You are seeing companies create algorithms that they hope will be monetized bubble in that they can provide some sort of value generating service across the large population.

You're seeing big tech create some algorithms. You've seen the work coming out of Google and Verily on [00:09:00] various aspects of say image analysis on retinas. Predicting diabetic retinopathy. So we're seeing all of this what I call government academia and industry stakeholders produce these things, but yet we don't really have a set of national guidelines and guardrails as to how these things move from bench to bedside.

Eric Schmidt, who is on the board of Mayo Clinic. A former CEO of Google and [00:09:30] Alphabet, and I were a onstage a few weeks ago and he said, how many bad sci fi films have you seen where the AI is let loose and the next thing the robots are enslaving humanity? So the problem of course, is we all have this feeling about what AI is.

Well, it's not that, but Eric just wrote a book with Henry Kissinger called the age of AI, where he reflects on the fact that as we deploy these things, we better have some guidance. And so I think as we see all these [00:10:00] wonderful developers coming up with good stuff, let's wrap it with some guidance.

Bill Russell: The data's really interesting to me in that we can't just pick up an algorithm from Mayo and drop it in Irvine, California, but still we're trying to develop these algorithms that are going to work. And it really depends on the dataset doesn't it? I mean, I was talking to a physician founder of an organization.

And what they're doing is popping this dumb cameras, camera you and I are using to talk in this way into the patient room. And they're feeding [00:10:30] that data stream into AI algorithms. And the thing I liked about that was it's almost a perfect data stream, right? It's just acting on computer vision, what it sees, does this pressure wound patient have they been turned?

And now it's very, it's very basic, but it's a perfect set of data. Whereas when we start talking about that EHR data, we have to take into account the variability in the geographies of those communities. For instance, Irvine, California will be about a 20% [00:11:00] Hispanic, about 15 to 18% Asian populations and various Asian populations, if you really broke it down further. Whereas Mayo population might be Florida if I thought about it.

I mean, you have Florida, you have Arizona and so when you take those populations they're very different. And we see this problem in big data and data science being the application and we see errors being made because of not really adjusting for the population [00:11:30] set. And so is there a way through that specific problem with regard to dealing with AI

John Halamka: Yeah. So let me describe three problems with our EHR data sets. So one would be just the fidelity of the data, and that is write a hundreds of data elements you don't know in what workflow, by whom at each institution they're recorded.

So what if don't know again, just make this one up that race ethnicity is recorded by a [00:12:00]registration clerk at three in the morning. And you ask yourself at three in the morning with a patient who's got horrible abdominal pain. What's the fidelity of the recording of some of this information.

Maybe it's not so wonderful. Well then let's also continue on the concept of race, ethnicity. One of the problems you have is the granularity of that data element. It doesn't really break down between, well, you're Asian. Well, are you Japanese? You're Chinese. You're Korean. You're you know my wife is Korean, right.

[00:12:30] And I would say an AI algorithm on Korean women may very well be different than an AI algorithm on Japanese women. So there's a, just a granularity issue. And then here's one real interesting problem. And I'm sure you've experienced. You saw a doctor for two minutes. And do you know when you read the physical examination note, they palpated every lymph node in your body and discovered it was normal.

Well, the challenges were [00:13:00] creating huge amounts of noise in the signal of the EHR data. We're recording through the use of sort of scripts and boiler plate and templates and that kind of thing. And then that's really hard when you're dealing with AI algorithms to separate the signal from the noise.

Bill Russell: Let's talk about the promise. The promise of AI. And then I want to come back to, where do these guidelines and guardrails [00:13:30] originate from. Yyou know, you talked about national and international, so it's not like we want we don't want ONC and HHS coming up with these potentially because you're talking about international standards.

So you need a much broader coalition of people, but we'll get to that in a minute. What is the promise? What's the promise of AI in healthcare? Why are we pursuing this?

John Halamka: Well, so let me give you an example, which I don't know that we've used on your show before, but it's just illustrative. So, you know, as a [00:14:00] toxicologist, I'm calling to a lot of, some somewhat peculiar consultation.

And so one night I was called and I was told, do you know, there's a 24 year old woman running uncloTHed through a Walmart parking lot at three in the morning. And the emergency physician discovered cannabis in the tox screen. Now okay. Every one of us as physicians practices by anecdote and intuition.[00:14:30]

And this particular physician decided that cannabis had this particular effect on 24 year old women. Now, what if an AI was able to look at a million women in their twenties with altered mental status? And be able to say, well here are the distribution of diagnoses for a person with that phenotype and genotype and exposal. Now it's not to say that there aren't substances that can cause [00:15:00] altered mental status, but cannabis probably doesn't cause that effect. This, it's a real case, right? Because the patient was discharged. Ultimately came to Boston, we did a lumbar puncture. She had the worst case of meningitis that anyone had ever seen.

And when I asked her about the cannabis, she said, oh, well, I had the photophobia, the stiff neck and the headache. And then I asked my roommate, if there was anything I could take and the [00:15:30]cannabis had nothing to do with the altered mental status and AI. Would have provided the clinician with augmented decision-making to understand likely diagnoses and likely treatments.

Bill Russell: So that's the promise and I love that promise. But when I think about, we talked about the data and the fidelity of the data and even the depth of the data. I mean, you start talking about phenotypes and those kinds of things, and I'm not sure, I'm not sure all the EHRs are [00:16:00]capturing all this data the same way.

And so it's, you might be able to develop, it might be as limited as you can develop. At Mayo, because you've captured this kind of data in this kind of way, and you structured it and you have the data governance around it to make sure that you are doing and the training for the physicians to capture it in a certain way. But how, I mean, how scalable is that going to be?

John Halamka: And so I think you're right, that if we look at algorithms going [00:16:30] forward, they will all need to be multimodal. And that means it takes into account, as you say, the EHR data, but maybe it also takes into account certain data from sensors you wear or sensors from your phone or sensors in your home.

And it may take into account certain laboratory tests that are esoteric, certain genomic markers, ideally, right. We'll get to a point where the algorithms say I need these inputs and it becomes a standard of care to gather that kind of information. [00:17:00] But for the moment, it's just, as William Gibson told us, the future is already here, it's just unevenly distributed. And so you're right. That not everyone is going to be able to have all these input variables for now.

Bill Russell: You know, John, I love the example of the simple camera in the room. And this has almost become a joke. Hey, the Fitbit, the Apple watch. But you're probably tracking this stuff.

And I mean, how far away are we from solid clinical use cases [00:17:30] with something around your wrist? Some sort of patch or that kind of stuff. Are we getting to real quality data that's going to be used?

John Halamka: So as I say, it's a William Gibson problem. So if you ask us say, well, so, Hey, how does Mayo clinic feel about gathering one lead ECG is applying algorithms to them returning a result and improving patient care. Oh, that was a last year problem. Right? And so we have 14 algorithms in the market of which we have predictive A fib. We have [00:18:00] objection fraction with an AUC of 0.92.

We can tell you from your Apple watch if your heart pump is weak, right. And it's all developed at Mayo with these various AI assigned data scientists, but it hasn't quite made it into standard commercial products. I mean, it's in a few places. Alive Core which is a little device you put on the back of your phone has Mayo clinic algorithms inside.[00:18:30]

For example. So, I think you're gonna look for 2022. I mean, it's just next year, within the next 12 months, you're gonna see many, many more consumer grade devices incorporating these AI algorithms to scale.

Bill Russell: Interesting. I want to talk to you about clinician burnout, but I want to go to the EHR question. You developed any EHR back in the day and I, I believe it might still be in use at your previous location. Do you consider the EHR a platform [00:19:00] or do you consider it more like a transactional system? And it has aspects of a platform, but it's not a true platform?

John Halamka: So am I allowed to say that it evolved as a transactional system and it has aspirations to be a platform? And the reason I say aspirations is because as you look at FHIR, CDS hooks. You look at various aspects of the interoperability and information blocking rule. You're starting to see the idea that you can have this thing that is [00:19:30] at the core, that has an ecosystem of partners contributing data and using data for novel purposes. It's not there yet, but it's getting there.

Bill Russell: So that's being driven predominantly by policy. 21st Century Cures and some financial penalties and those Catholics. So it's being driven by policy. It feels to me like it's going to be enough. It's still moving a little slow, but it still feels to me like it's going to be enough.

That policy change seems pretty, [00:20:00] pretty far reaching in terms of really forcing the players to take that next step. Would you agree with that?

John Halamka: So, yes. But I describe the perfect storm for innovation as an alignment of technology, policy and culture. Right? So the fact is we needed FHIR right?

You needed APIs. You needed restful transactions. You needed a policy that said these must be implemented and can't be blocked, can't [00:20:30] be charged for, and that kind of thing. But then you need a demand. You know, cultural expectation that these things will be of value or utility. Remember back 2008 through 2011, Google created Google Health.

This was the first incarnation. And they created actually a really nice platform for patients to record their data. And how many people used it? No one. Because it too much effort to get the data in. No value in the [00:21:00] ecosystem. So I think we are approaching a point where, oh, there are enough apps, algorithms maybe even your insurer will offer you a discount or special benefits if there are certain kinds of data you contribute. I'm thinking about what Aetna has done with Apple in the Attain program. When that cultural change occurs, that's really, what's going to get us adoption.

Bill Russell: Yeah, it is so hard. I was talking to one of the startups and they said, look, you can grab your [00:21:30] medical record from anywhere.

I said, all right, let's, let's start. And so I downloaded their app right there in their booth. It was so simple. It pulled up like 180 different providers. You have to go through there, find your provider. Then you have to remember your login to their portal to actually authenticate that you're you to that health system.

And then you start pulling that information. Oh, and by the way, you have to do that for every provider you, you visited in order to pull all that stuff [00:22:00] together. And not only that, one provider I visited in California, which was my providers, who I was a CIO for, the health systems and the medical groups were on different EHRs.

And so for every different specialist and somebody in that medical group that you were to visit, they weren't necessarily on the same system. So you had to do that four, five or six times to get that. It's still not what I would call, what's the word we use. It's it still has a [00:22:30] fair amount of friction to pull that entire medical record together. Is there something on the, on the horizon that's going to reduce that friction? Cause I mean that friction almost makes it not even worth my time.

John Halamka: Yeah. And so for example, I happen to have an Android phone in front of me and this Android phone happens to have an app called Common Cealth, which is free. And Common Health, again, not disclosing anything here. It's, it's [00:23:00] fine is able to reach all the FHIR interfaces about me and then download in real time, all the information. So for example, there you go, there's my QR code showing you my three Pfizer's right. So it's great. If you have a smallish number of providers, because yes, it does use OOFF.

And does do the linkage to whatever your login and identity is at the provider organization. So here we have a free app that's fully integrating all of my [00:23:30] data and it makes it portable. It's all good. But do we have a canonical identity management program in this country? A mechanism of saying I've linked my biometric fingertip or eye or face to an identity and therefore it's going to go out and fetch all the stuff about me?

No. And, the technology isn't the barrier to be honest. It's just, there isn't an appetite. A use case at the moment for a nationwide mechanism [00:24:00] of being able to share identity.

Bill Russell: Yeah. Get getting back to the EHR. What do you think the future of the EHR is? I mean, do you think it just sort of goes into the background, acts as a repository, does its transactional work great within the health system itself. It is opened up via FHIR. Or do you think there's going to be a new like ground up something that we're going to see in maybe the next five years or so?

John Halamka: Have you spent time with Don Berwick? [00:24:30]

Bill Russell: I have not.

John Halamka: So he was our CMS administrator, but he founded something called the Institute for Healthcare Improvement. The IHI. And Don would use the term sometimes you engineer a system to achieve exactly the result you got. Now, let's think about what we did during the meaningful use era.

I mean, I was part of that, right? We said, oh, well, we want to do population health. We want to do immunizations and we want to [00:25:00] do quality and what we ended upwith was a set of requirements that required every physician at every encounter to record 141 pieces of information while seeing the patient making eye contact and being empathetic.

And we got burnout. Oh, that's a shock. We got exactly the results we engineered. Now. The dream is not just lipstick on a pig, so to speak. Remember I run a farm so I can say that but to say that we're just doing the EHR as it is [00:25:30] today. And we're putting some FHIR interfaces and maybe a little bit more decision support, not good enough. What I think we need exactly as you described it as a complete paradigm shift in the way that medical records are recorded. How about this? A doctor and a patient have a conversation. The computer, of course, with everyone's consent, whether it's audio video, or both is recording that conversation. And the end result is through NLP and ML.

What you're doing is taking the unstructured conversation, figuring out who said what, [00:26:00]putting it into some structured, coded form, and then using it for various purposes, whether that's clinical care, research, billing, whatever. Because this idea that the human is transcribing a deep conversation into a set of discrete data elements is not going to get any better. We have to change the paradigm.

Bill Russell: Interesting. I wouldn't do this to you, cause you're living your best life at this point, but if if you were some [00:26:30] VC came along through a ton of money at you and said, John, ,we need you to, to lead in this EHR revolution. I assume when you wrote your EHR back in the day, you, you designed it.

I mean, maybe not on purpose from a platform perspective, but you decided to integrate, you decided to share information with the other departments, with the patient, with, I mean thats how you think, right. Even though meaningful use called for something [00:27:00] less than what you designed for. That's how you think. So let's fast forward. Today.

Here we are. The EHR market is essentially consolidated to I don't know, let's call it a half dozen players. And we're saying, all right, we're going to fund this. We're going to get to the people we're going to put the team together. Where do you start? I mean, what you just described is a great use case, but I can, I can tack on Nuance onto their DAX ambient clinical listening onto any of the six platforms that are out there today.

And I, [00:27:30] could get close in certain fields, right? I'm not all, all the specialties, but in certain fields I can get there. Where do we start for rebuilding what an EHR could be?

John Halamka: So I guess one of our challenges is regulatory complexity. Right. And so think about it. I mean, I worked on this self-built EHR in the 1990s, when clinicians could invent things for clinicians, nurses, for nurses, pharmacists, for pharmacists to [00:28:00] support what they felt was optimal workflow, safety, quality, transparency, and that kind of thing.

I think it's just really, really hard to create an EHR Denovo today because of the set of regulatory, complexities and requirements to ensure data and treasury prevent fraud and abuse, meet meaningful use, right? All these other things. So that's the interesting issue cooker, even with this extraordinary ambient listening, natural language [00:28:30] processing and AML achieved compliance with today's regulatory framework.

It would be tough. So I think we have to take a careful look at what it is we want to achieve and engineer what we want to achieve that will require regulatory change.

Bill Russell: It's interesting that that's where it would start. It's almost like going back to where it started, but in all honesty, I mean, we started on this technology path and no one was [00:29:00]adopting. It needed a, it needed a policy push. Otherwise we'd still be looking at 60% of hospitals are digitized today instead of the number, which is well over I think it's over 90% now.

John Halamka: Yeah, it's high nineties, but so, but here's the issue and sometimes you have to do the wrong thing to get to the right thing. Right. So think about you may not remember the CCD and the CCDA and all these XML forms that were used as is interoperability.

Bill Russell: No. I do [00:29:30] remember.

John Halamka: Yeah. So we argued way back 2005, 2006 for APIs. And no one in the industry thought an API would be a reasonable thing to do. We are so happy with HL7 V2. Why don't we make H7 V2 an XML? Have more data elements? It's very comfortable. It's very safe. Well, it was only when we discovered that anyone can generate a CCD and no one can parse one.[00:30:00]

That anyone was oh wow, we made a horrible mistake. That API thing, it's exactly the right thing to do. Now we got there. And I think we had a market failure in the adoption of EHRs. We needed a regulatory change. And now that we've got market acceptance, we need a radical revision of how those things work.

Bill Russell: All right, John, I want to talk about clinician burnout. We've talked about anecdotes. We've talked about data on this. But at the end of the day we have the great [00:30:30] resignation. The nursing shortage is a thing and projected to be about a half million shortfall in nurses in the next three years.

This isn't even a ten-year projection. It's a three-year projection. What role do you think technology is going to play in addressing this challenge? I realize a lot of other things are going to be at play here, but I want to focus in on the technology. Do you think technology has a role to play?

John Halamka: A huge role. Right. And so there's two aspects of technology I would reflect on. One [00:31:00] is today. And I'll just give you a Mayo clinic example. For radiation oncologists to create what's called natto contouring profile, a radiation dose through a linear accelerator on a complex tumor, requires about 16 hours of work. Mayo clinic has, working with our collaborators at Google, developed an algorithm that can create an auto contouring profile for radiation oncology in one hour.

And then a human reviews it. Cause [00:31:30] the physics and the math, it's just done by an algorithm, which is reasonable. And so the question is, can you augment your humans and make them wildly more productive because they're doing review rather than authorship. So that's certainly something technology can do, but there's another aspect and that is practicing at the top of your license.

You may know that Mayo clinic launched last July Advanced Care at Come. The idea that we can do serious and complex care in your living room. And that care is delivered by EMTs [00:32:00] and paramedics supplemented by specialty physicians, working for a virtual command center and algorithm. And so ask yourself that question.

Well, if humans can work better, stronger, faster and each human can work on just the stuff they're uniquely qualified to do, we'll probably be able to get through the great resignation.

Bill Russell: Yeah. I mean, that's phenomenal. 16 hours to one hour is definitely the kind of thing. That's [00:32:30] pretty amazing. And I, I was talking to a bunch of ophthalmologists of all people and they were just talking about how much of their work is just straight up math, figuring out what lens to use is, I mean, they collect all this data and then just do straight up math.

And it turns out that computers are really good. you know, If we build the right algorithms. I wanted to touch a little bit on, the Google partnership and how that's going. I thought, you shared this before, and I find it very interesting that you've created these layers of access to, [00:33:00] to the data.

I thought that was really interesting. And I'd love to just cover it again before we talk about the partnership and what you expect to see in 2022. One of the challenges we've always had is these partners come in and say, Hey, we're going to write some great algorithms against your data, give us your data.

And we always sat back like, oh my gosh, that's a huge amount of exposure. It's like, oh, well, we'll run on your, on your network. It's like, all right. Yeah. But we still have all sorts of challenges, but you've addressed that [00:33:30] with your architecture with Google. Can you share a little bit of that with us?

John Halamka: Well, absolutely. So here's the architecture. First we moved 10 million longtitudinal persons records into Google cloud and de identified them and did it to a degree never achieved before. Right? So it's not just for moving the name and the address and the phone number. It's actually going into every record and changing proper nouns, such as job role or geography or familial [00:34:00]relationships. Something we call hiding in plain sight. Instead of saying this leading healthcare IT journalist, it may say this engineer who you know, does writing or something, whatever. Right. It's just, it's vague enough that it is not read identifiable and that's sort of step one, step two. It's sitting in a cloud container. Google doesn't have access to it. Only Mayo has the keys.

We create sub containers of it and then invite [00:34:30] collaborators, joint ventures, and partnerships into their secure sub container, where then they can run algorithms against the de-identified data. But actually can't take the de-identified data. Nor can they link external databases to the de-identified data to try to re identify.

So this multi-layer defense, we call a data behind glass strategy and so far so good. Right? We've got about 30 partners, validated algorithms, new [00:35:00] visualizations, new products, but no data has ever left with a customer.

Bill Russell: That's fantastic. So talk to us about the Google partnership. That was a ten-year arrangement. And if I'm not mistaken, was that two years ago that that came together or was it, was it longer than that?

John Halamka: Just finishing our third year.

Bill Russell: Okay. So you're finishing your third year. Got another seven years. What is, what does 2022 look like for that partnership, do you think?

John Halamka: Well, so think about the nature of what we want to [00:35:30] do, given that we have this amazing cloud computing and storage environment is creating more multimodal data and more algorithms from that multimodal data, especially in the field of various images.

Right. So I will tell you that in literally one week we will have finished loading every historical Mayo clinic image in every modality, into our de-identified database in Google. [00:36:00] And let me tell you why I think this is interesting. I'm going to give you a personal example and you know, that I always share things very transparently about me personally.

So I have glaucoma. This is why I'm very interested in your ophthalmology example. I've lost 25% of my vision in my left eye. Mayo clinic. It says, well, that seems a bit odd because you buy a maximum medical therapy, very compliant. Why are you losing vision? We better do some imaging of your brain.[00:36:30]

Maybe there's something funny going on there. So they did 6 MRI sequences in my brain. They found them my third ventricle, which is a cistern of fluid in the brain is larger than a normal human. So I said, Hey, do you have any idea what the Gaussian distribution of third ventricle sizes and a normal human?

And of course the answer is no one has any idea. All I can tell you is, well, I've seen 20 and [00:37:00]yours is bigger than those. All right. So question is can Mayo, working with Google, create a map for the world of the distribution of body composition to understand what are the sort of two standard deviation limits on any body parameter. And we've already started that.

Bill Russell: So let's assume I'm a health system CIO at this point. I'm listening to this and I'm going, Hey, [00:37:30] you know what I would like to, I'd like to be a part of that. I'd like to add our data set to that. You have some of these players out there that are collecting datas from data from a lot of different health systems and what not. But if they hear this and they go look, I would like our data set. I'd like to participate. What does that look like? Is that a possibility?

John Halamka: Absolutely. And so this is a fascinating debate and I don't know what the right answer is, but I have, of course my own biases, which is there are those in our [00:38:00] industry that say let's create a massive centralized data store under the data governance of one entity and then run algorithms on it and create products.

I think that has a whole lot of security, privacy governance, and even psychology challenges. My notion is Federation. And so what I have been working on, and we're just finishing a pilot on this is to [00:38:30] say, and arbitrary data provider, not at Mayo can interact with an arbitrary algorithm. That might be at Mayo or not in a way that protects the IP of the algorithm and the privacy of the data.

And therefore we don't need a huge amount of lawyering and the data doesn't really leave your firewall and the algorithm doesn't leave my firewall. They're able to interact and a mechanism of using cryptography and advanced [00:39:00] mathematics. So suddenly you can say, oh, well, I'm in Louisiana. And, the population Louisiana, a little different than the population in Minnesota.

Let's try our data set against your algorithm, see how it performs. And I can do it without having you sending me your data.

Bill Russell: So does that require me to move my data into Google's platform? No it doesn't. You're saying federated wherever.

John Halamka: Right? So you'll, you'll get more on this next time we speak cause we're just finishing the [00:39:30] pilot, but we are convinced that Federation is the right approach and we shouldn't be able to do it without moving the algorithms or the data.

Bill Russell: That's interesting. What if I wanted that architecture that you're talking about with Google? I thought, Hey, this is pretty interesting. Move that data into that, that kind of that kind of architecture. Is that the kind of thing that you've public domain that architecture? Or is that the kind of thing that I can participate some way in that?

John Halamka: Well, [00:40:00] absolutely. Right. So, Google is a very creative company and I don't know that they have a skew for, Hey, let's build the containerized healthcare data, order three of those.

Bill Russell: Do they have a skew for anything? I, I wouldn't imagine.

John Halamka: Right. But point being, they'll do this, right? I mean it's and whether you choose Google or Microsoft or other provider, I mean, the answer is what we have done. I mean, it isn't exactly proprietary. [00:40:30] I mean right. It is a approach using cloud technologies. Maybe it's the combination of our various partnerships that have created such things as that de-identification approach that gives us a level of confidence and security. So sure. I would encourage Federation with us collaboration with us and more and more. I haven't got to see that intuition will be replaced with information [00:41:00] and all of us have to work on that.

Bill Russell: John, how's the farm?

John Halamka: So far so good. You know, It's cold.

Bill Russell: In Massachusetts. Yeah I would imagine it is cold.

John Halamka: So here's the challenge, right? Keep water liquid for 300 animals when the temperature approaches zero.

Bill Russell: You just gotta keep it moving don't you?

John Halamka: Well or heated or whatever. Right. And so, so as you think about life on the farm you run and just again in Massachusetts, where I [00:41:30] am, the temperature varies from negative 10 to 110. And you need to be able to keep animals healthy and happy through that hundred degrees of variation.

Bill Russell: So do you have a team helping you? I mean, you're taking care of a lot of animals, your sanctuary for these animals. I assume you have some volunteers and some help.

John Halamka: 500 volunteers.

Bill Russell: Wow. Do you have to manage them in any way?

John Halamka: Yeah. So think about it this way, I do the facilities management, the [00:42:00] IT and the primary care. And my wife manages all of the volunteers, all of the education and all of the programs.

Bill Russell: Wow. The volunteers. Are they educated before they come in or do you, they're just kindhearted people that show up?

John Halamka: Well, so of course you would imagine given my 40 years in the industry, I have a comprehensive certification program. And so we have five levels of certification and you come in, we offer courses and we have [00:42:30] weekend trainings and that kind of thing. You start with goats and sheep. The goats and sheep actually only have one set of teeth.

And so you can put your finger in the mouth of a sheep or a goat, and you'll still have your finger. As opposed to a horse or a cow full set of teeth, don't do that.

Bill Russell: The level level one is keeping them safe, making sure they don't get injured.

John Halamka: Right. And so what we'd have to do is levels of education and training that take you from the earliest entry of you can [00:43:00] groom the sheep to the, you can help with exercising horses or calves.

Bill Russell: And this we'll close with this, but in this day and age, I'm talking to so many people that have so much going on and you have a ton going on. I mean, I, I read your name. I see. You're, speaking. You're here. You're there. You're doing a lot of stuff. Is this relaxing for you having 300 animals when you come home?

Or is this, I mean, does this, raise your, your blood pressure as, as you have to deal with that and a pretty demanding job.

John Halamka: [00:43:30] So, if you visit me in Massachusetts, I'm going to ask you to groom one of the donkeys. And after you grown, one of the donkeys, your heart rate will be in the fifties and you will be feeling like, Hey, I'm pretty creative.

Let's go do the next thing. So caring for these is not only a benefit to the animals, but it's a benefit to the whole community because in this time of COVID, do you know that we have actually become a rescue for people? Because the [00:44:00] level of anxiety in society is so high. And when you spend an afternoon with a donkey or a goat or a horse, your anxiety is a lot lower.

Bill Russell: Because those, those animals don't know that COVID is going on do they? They, they just. Man, that's I am going to take you up on that at some point, I'm going to reach out to you and say, I'm going to be in Boston at these times and see if we can coordinate. I'd love to get out there and see what you are doing out there. And the 500 volunteers, I think it would be fantastic.

John Halamka: Well, we'll [00:44:30] put you to work.

Bill Russell: Exactly. I would expect nothing less. Hey John, thank you again for what you're doing for the industry and thank you again for taking the time to visit with us. I really appreciate it.

John Halamka: Well, any time, stay in touch and be well.

Bill Russell: What a fantastic discussion. If you know someone that might benefit from our channel, from these kinds of discussions, please forward them a note, perhaps your team, your staff. I know if I were a CIO today, I would have everyone on my team listening to this show. It's conference level value every week of the year.

They can subscribe on our [00:45:00] website this or they can go wherever you listen to podcasts. Apple, Google, Overcast, Spotify, Stitcher. You name it. We're out there. Go ahead subscribe today. Send a note to someone and have them subscribe as well. We want to thank our Keynote sponsors who are investing in our mission to develop the next generation of health leaders. Those are Sirius Healthcare, VMware, Transcarent, Press Ganey, Semperis and Veritas. Thanks for listening. That's all for now.[00:45:30]


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