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 Thanks for joining us on this week in Health IT Influence. My name is Bill Russell, former Healthcare CIO for 16 hospital system and creator of this week in Health. IT a channel dedicated to keeping health IT staff current and engaged. Today's guest is Dr. John Halamka, president of the Mayo Clinic Platform.

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That is Dr. John Halamka, president of the Mayo Clinic platform. Good afternoon, John. Welcome back. Great to see you. Yeah. Just for the record, I pronounced your name, Halamka, and someone corrected me once am am I saying it right? The answer of course is depends on the country you're in. And so Halamka is how a, a US person would say it's all fine, wrong.

They might say something slightly different. But if you're in Hawaii, I'm sure it would be Halaka. . I, I, I've heard a couple pronunciations. I just wanna make sure that, uh, that I'm not saying it saying it wrong. Oh, just as it's spelled. It should be Smith. That's a bad last name to have. Everybody in healthcare's pretty busy, so I don't want to minimize that, but.

You're getting hard to keep up with. You have the Medically Home Co-Investment with Kaiser Vaccine Credential Initiative. Two new data companies launched c Ovid 19 Healthcare Coalition. And uh oh, by the way, I did just get your latest book, the Digital Reconstruction of Healthcare, transitioning from Brick and Mortar to Virtual Care that just arrived yesterday from Amazon.

So you're staying pretty busy. So look at this way, I've written a couple of articles over the last decade on what I call the perfect storm for innovation. And that is when government, academia, and industry align, and there's a sense of urgency to change. I could argue that this era, which is maybe we'll call it the covid new normal, or at least approaching the covid new normal, has regulatory rollbacks and waivers.

Has a culture change that people are expecting more care at a distance. The rise of digital health and investment like we've never seen before in the life sciences. This isn't just a perfect storm for innovation. This is a once in two lifetimes opportunity to change. Yeah, it's.

It's amazing how many things have changed in 18 months. And of course I can give you plenty of details, which I will in a second. But let me make it very personal. My mother is just about to turn 80. Do you know that my mother was not a user of digital health? Virtual health, in fact, really felt that this iPad or laptop was really getting between the doctor and the patient.

Well now of course she had the experience over the last 14 months, but you couldn't get care unless you accepted virtual health. I mean, for the usual kinds of stuff. Obviously emergent surgery or something. Sure. She says, wow, I've been able to get the care I need with the same quality, safety, and outcomes.

He's the new normal. So sure we're not still at 95%, but we've gone from three to four to 25 in one year. Again, how many times in our history are you seen adoption of a technology that goes up by 5, 6, 7 times in a year? And part of that, as we found with medically home, as you say, and the advanced care at home.

The regulatory rollbacks enable us to have care distance, enable us to have different licensure and different levels of practice, but reimbursement that's near parity, right? So the problem with telehealth before covid is you could never cover your costs, so you couldn't scale. And I can tell you from every conversation I had with H-H-S-C-M-S-O-N-C, there's no desire to roll back these changes.

Cultural imperative. Absolute reimbursement and policies that suggest we can do this forever. All good. We're gonna go into all those things. We're gonna go into medically home in depth, but I, I wanna, I wanna lay the foundation for this. So the Mayo Clinic platform, the concept of a platform is probably why we're talking about in addition to all these things, but.

A platform enables us to move quickly, much more quickly than individual applications, individual data stores and those kind of things. So talk about the concept and the vision for the Mayo Clinic platform. So Bill, this is probably one of the hardest things for people to understand. They say, oh, well I see you put a whole bunch of businesses together under one roof and it's a platform.

Well, no, that's a collection of pipeline businesses. So let me give you an example, which you're gonna find very odd, but I am at Unity Farm Sanctuary surrounded by 300 animals. So this is a John Deere 2 0 3 2 tractor. Happens to be the tractor we use. John Deere was Lasic Pipeline company. Which is it bought a bunch of parts from all over the world, assembled this thing, sold it to a customer, done.

That is a very linear, if you think about it, supply chain flow from the tire manufacturer to the tractor builder to the customer, and there's no additional revenue. Several years ago, and Harvard Business School case has been written about this, John Deere said, let's become a platform company. Say, wait a minute.

How can a tractor builder be a platform company? So here's what they did. They instrumented every one of their larger tractors with various sensors that could measure GPS. Where are you on your field? How fast are you going? How's the engine running? Oh, and then they got water telemetry so they understood.

Hmm. Let's see how much rain you had and then how much work you did. Oh, I really started getting commodity prices of wheat and soybeans and corn. Today they have the largest database for precision agriculture that exists in the industry. And you say, wait a minute, you know precision agriculture? Yes. In fact, we can tell you what the commodity price of corn two months from now will be.

Because we actually know who's harvesting, how much rain they had, what the RPM of their tractor is. So it isn't just John Deere selling data to its customers or building better tractors. Now there are are third parties coming to that platform and building all kinds of new apps, new analytics, even stock market forecasting.

John Deere is now a platform data company. And for every new tractor, they add more telemetry, comes to the platform, making it even more valuable. So that's a way to think about a platform. We'll get back to our show in just one moment Every day, you're using your skills to help end substance use disorders within your community.

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That's BHW as in behavioral health workforce.hrsa.gov. Now back to our show. So talk about the elements that go into that. For healthcare, I think this is this week in health it, I think people understand the elements in that it is data, it's advanced analytics, it's advanced capabilities, artificial intelligence and machine learning.

I'm trying to envision what other aspects it's, it's probably also processes I would imagine as well. I mean, what, what are the elements that come together around this? So let me give you four broad categories, and these are just categories, right? These aren't specific products, but they're categories.

Would you agree that we have a huge amount of new data sources because of the devices we wear, the devices we carry, the devices in our beds, the devices in our homes, right? All this data, lots of it, continuous, high velocity data. How are you gonna gather it? How are you gonna curate it? How are you gonna normalize it, put it in standards?

So the first set of platform components I call Gather, right? It's gathering all these new data sources from wherever they may be. And uh, unfortunately lots of them aren't in standard form, so there's just a lot of work to do to clean 'em up and to put them into a universal schema so they can be used for the next step, which is discover.

Imagine you have 60 petabytes of data. That's a lot of data. How are you gonna find the signal on that noise? How are you gonna answer a question a clinician has today? How are you gonna do precision medicine? Well, you need large de-identified data sets, again, in a normalized schema and the tooling that is necessary so that you can create an AI factory.

If you woke up this morning and said, oh, I have this theory, I bet that people who have a positive covid test probably had certain characteristics. Oh, and long covid, maybe that has certain characteristics. Well, you need the tooling. To be able to explore that on very large scale. De-identified dataset.

And then you develop an algorithm. How do you know it's fit for purpose? And the joke I tell, which it's just, of course not true, but it illustrates the point. We are going to use the data of Minnesotans. So it's a million Scandinavian Lutherans and we're gonna create an algorithm to look at some disease, and then we're going to apply it in Georgia.

Is it gonna work? I don't know. Right? So you need a set of componentry that looks at bias, efficacy of the area under the curve. How well does this stuff actually meet the need of the patient front? And finally, we're gonna, once we have the algorithms and the analytics and the visualizations, how do we deliver that?

in a workflow so that a nurse, a doctor, some kind of extender can get the benefit of whatever decision support you're offering. So when you look at Mayo Clinic platform, it broadly falls into those four categories. When you look at our co-investments and co-development, the things that we've launched as joint ventures.

Yeah. So I'd, I'd like to go into the Kaiser co-investment with a. And I, I mean there, there's so many things. This is really exciting. Why don't you first talk about what medically home's doing and how you gotta this, how you gotta this point because you started working with them before the significant investment with Kaiser.

Talk about how this came to fruition. So ask yourself the following question. Suppose you wanna put a patient who's serious and complex and acutely ill into a care process in their homes. What are some of the problems you're getting to address? Well, first, is that even a good idea? . And so medically homes worked for eight years in actually answering the question of who is gonna do well in a non-traditional setting.

Now, I'll give you an example. Congestive heart failure, chronic obstructive pulmonary disease, pneumonia, all serious disease. But you know you're not gonna go bad in two minutes. You're gonna have signals that say, oh, shortness of breath getting worse over 24 hours, or fever spiking or something. How about somebody with v ttac?

Right? You get a heart arrhythmia that one minute, you're fine. The next minute, your heart's not beating. Not a good candidate for a hospital at home, . So the answer is, 30% of patients in hospitals today are good candidates for care in a non-traditional setting. So that, so that just sets 'em up. That's just saying that that population is candidates, but it's, it, it still doesn't establish that we're gonna care for them in the home yet.

Absolutely. Right. But 30% are candidates, and then you have to do things like social determinants of health. Well, what is the nature of the home? Is the home safe? Does the home have reasonable power? Does the home have access? Right. If it's a fifth floor walkup, that's not so great. Or do you have any kind of internet capability, whether that's cellular, broadband, satellite, whatever.

Right? So what we have to do is say, disease, state, comorbidities, demographics, okay, you're in the 30%. Now let's go do a site assessment. And that side assessment is either a yes or no. And then of course, think regionally. Do you have the skilled staff you need? And Mayo Clinic has established a training and certification program, so we're building a whole new workforce of, say, EMTs.

They get upskilled so they can do community paramedic work in the home, deliver. Bring supply chain, setting up electronics, putting the bed that you need in place, all that stuff a a. And so what you end up with is as long as you hit these criteria, you can deliver very effective care at a distance as we have for over a thousand patients in Florida and Wisconsin.

And we're expanding next month into Arizona. Wow. Uh.

We have, we, we have other systems that have done similar work to this, obviously up there in Massachusetts as as well as others. What's the data to support that? It is better or it is comparable and less costly to do it this way. So as you can guess, we've done substantial analytics on every patient we've seen, and the mantra at Mayo is, start small, think big, move fast.

So we did one patient at a time, , and then we did a deep dive. Oh, what was the outcome? What was the quality? Oh, do we have a supply chain issue? And then we started doing measurement, like not only the usual quality indicators and such, but patients and families going through a full net promoter score evaluation.

Would you recommend this to your friends or your family members? So mathematically after the first several patients and series of process improvements and then ramp up and all the rest, what we end up with is, to your point, the analytics that say, oh, every quality measures same outcome, same safety, same cost.

We cost computations and healthcare are a science , but you don't have the bricks and mortar . sunk costs. When you're talking about a home, you don't have the heat, power light, you don't have all the janitorial staff, all that other stuff. So you do a cost computation and it looks like about half net promoter scores.

You measure 'em. 97% of patients and families who went through this experience would recommend it to family and friends. There's no way any hospital in this country is gonna get a net promoter score of 97%. No, that's, that's amazing. But it, now I'm thinking, okay, let's scale this up and would I bring this to my health system and what, what would I have to overcome?

I think the biggest thing I would have to overcome is in the administration. I'd have to go into the administration and say, look, we're gonna be giving up a 20,000, $15,000 visit, whatever it ends up being, and we're gonna get a 7,000 visit. Isn't that one of the challenges? Well, not necessarily, and let's talk through this in a couple of different ways.

So first, if you had a risk arrangement, any kind of Medicare advantage, right? Right. If you say, I can now deliver the same cost, same care with lower cost in any kind of risk arrangement, they say, oh, that's great. We need more of those. That's, that's wonderful. But here's what we found, especially in a time of covid, you found that hospitals were saturated, right?

There was no capacity. So if you say, oh, I actually can take my non covid patients, put 'em in non-traditional settings, deliver care, fill the hospital with just covid patients or high acuity patients, whatever, people say, oh, wait a minute. What that means is I actually am able to use my bricks and mortar facility to its greatest extent.

That actually from a business sense makes easy to convince people. You know, there's one element to this and that other element is imagine that you have an immunocompromised patient, 87 years old. This is actually a true story, and you say, oh, no problem ma'am. I'm gonna bring you to a place with lots of people coughing on you.

Right? It turned out to be medically even better to care for a whole lot of these patients. In a non bricks and mortar facility, so it made sense to a hospital administration. We're looking at, hospitals are still putting up towers and, and part of that is we just came through a pandemic where we were worried about not having enough beds.

But in your book you talk about, Hey, you know what, you're gonna wanna take this into account because again, 30% are eligible. So let's just say 20% of those visits might be done out.

Are Mayo Clinic or whatever beds in that community without adding a single, you know, room in, in the hospital itself. How do you sort of balance those two things? When the pandemic, we were worried about not enough rooms or the right type of rooms, I guess, and the nature of healthcare is changing. We can do higher acuity out of the home with a certain level of effectiveness and it's going to look different moving forward.

How do you balance those? Well, and so to your point, in several of the sites, we've rolled this out. They say, oh, well, we have a bricks and mortar facility. We can't add beds. It's physically constrained, right? Don't have enough land. Or the capital that is required to add beds is so high, we just can't do it.

You say, how about this? What if you could see 20% more patients without investing capital or worrying about your physical footprint and use your existing staff? Oh my God, that's amazing. It's like one of the only ways for a healthcare system to expand. It's economic impact for a community. And so we certainly also found that that was a very valid use case for hospitals that really just didn't have a lot of capital.

As most community hospitals don't add a 97% net promoter score as well, which is kind of amazing. So talk about the logistics of it using your. Using MT or training EMTs, are we literally seeing doctors and go ons to peoples homes utilizing. So typically the model is this. What you have is the EMT doing the physicality of the home configuration, a visiting nurse who is doing medication administration, bandage changes and those sorts of things.

But the clinicians, the specialists, and the hospitalists, the intensive, they, they all are working remotely and so they work out of a command center. Or run with this cloud hosted software from wherever they might be. The dashboard is available in a cloud hosted accessible fashion. And so what that also means is that you can take an individual clinician, you could scale them larger and, and so I, I would argue that as we train our next generation of docs, we're gonna see a specialty called a virtual list.

Right, like a hospitalist, but you're a virtual list. You got all the telemetry. It's like an F 15. You're flying and you can be anywhere and you can deliver the same care, and you got people who are in the home who can be your hands and in feet if need be. I, I do wanna talk about the book, uh, a little bit.

When did the book come out? Came out on June 8th. You're very timely. Wow. Okay. So talk about the title a little bit. Digital Reconstruction of Healthcare, transitioning from Brick and Mortar to Virtual Care. That's what we're talking about right here. Arm. Yeah. 'cause what we've seen is that just as John Deere became a data company.

I am gonna argue that in the next decade, and it may be even faster than that, that hospitals in general healthcare as an industry has to recognize it's a data business, right? It's figuring out using algorithms, who needs what care, in what location, at what level of intensity and cost, at what time. And so you'll see an explosion of algorithms of data aggregation, of new apps and workflows that give the patient and their family access to what they need when they need it, at the cost that is understood and predictable.

And it'll be based on evidence. I, and this is an interesting thing as a doctor, I'll tell you, right? So I have been a doctor for 35 years. We were all trained as apprentices. We weren't necessarily trained based on data. So when we make decisions, we make decisions based on the cases we've seen or what our chief resident taught us 35 years ago.

And so what that means is that although doctors are amazing and of course are trained in physical diagnosis and such. , they don't necessarily always get that right care plan based on purely the experience of being a doctor. You need more, you need data. And, and that's where hospitals are transforming and that's the title of the book to, well, how do we take our data and use it in novel ways?

How do we incorporate algorithms and workflow? How do we think of new care models? 'cause that's really what healthcare has to become. It's interesting back in, uh, 20, uh, 11, when healthcare. We talked about increasing the number of touch points, and then I learned this term longitudinal patient record.

Once you get into healthcare, they load you up with all the terminology and the jargon that you need, but it's an important concept of that data. And just the first chapter is digital reconstruction necessary. You answer some of the questions, but you have this section where you talk about episodic medical care often falls short.

I want.

Talk about Firefly Health, which you're probably familiar with. Oh, and he, and he was talking about their, and it sort of in its pilots phase and, and they were saying on average their customer talked to a, a clinician of some type, 65 times less. It's just an interesting concept in that they made it so easy that people started to use it.

They were doing things like if they were in the grocery store aisle, they would use that service to say, Hey. Whatever is, should I, is this good for me, given my condition or whatever like that. So anyway, 65 times they, so they're getting a ton more information and he now just launched a new company called Zeus Health.

It's longitudinal, which is addressing that longitudinal, uh, patient record as well. You talk about data really changing it. The challenges with episodic care. Let's, in that context of those two things, and I'm not sure phrased this question real well, but I'm sure you have an answer anyway. Episodic medical care often falls short.

How is digital gonna help? How does it fall short, and how does digital really help with that? Oh, and to your point, the data we are increasingly putting into our AI models is multimodal, right? It isn't just, you saw the doctor one time, it's Oh, what have your labs been over the last decade? What's your weight been over the last decade?

What are the comorbidities you had? Not this visit, but three visits ago, right. In the model, for example, we have 60 algorithms that we've developed in Mayo Clinic platform, but one is a breast cancer prediction model. It has 84 inputs, right? And so we gonna predict which women will develop breast cancer and offer them treatment today so they never develop breast cancer.

it requires 84 input variables, and those are gathered over a life of experience. I mean, it's right, it's structured, it's unstructured, it's telemetry. I'm gonna use a funny term, the exposome, you know? Right. What? What did you, where do you live? Oh, you live in a dusty place where you're breathing grime. All of these things have to go into the algorithm.

I really was. Now, would you say that if I went to the doctor and had an episodic visit in January and say, yeah, whatever, it's the flu. It's a cold, it's not enough. You need to know my travel history . The last 90 days I didn't have covid, by the way. I'm still covid negative, vaccinated all good, but right.

You could see how it's your longitudinal experience, your travel history, your, oh, what do you do for a living? Oh, you live on a farm? Oh, well I didn't think about this sheep disease that you could possibly be carrying. You have to integrate all that stuff to get the right diagnosis. You know, , I wanna get back to the farm because I, there's this new thing that's happening where people are foraging for stuff and I thought, these people are gonna kill themselves if they don't have Dr.

Halamka knowledge, they're just gonna go out and like pick poison and we'll get back to that later. It's, it's just a side thing. For my knowledge. I was reading this article thinking, I think these people are, I understand knowledge to go mushrooms. Doing with some in. I do nine televisits a year. I've done five today.

And, and so to your point, I mean, again, we can talk about this later, but here, if I were to, uh, just, uh, take my email for, oh, the last half hour or so, and I'm just, I'll find you a good mushroom photo that's just coming, like my three year old ate this. What am I gonna do, ? Yeah. Let's get back to an area where I'm, I'm more comfortable.

And that's, so we go into chapter two.

I, I wanna skip over the, the virtual visits, the urgent care visits and whatnot that's been talked about really ad nauseum. Not that it's not important, it's really important, but I, I wanna talk about remote patient monitoring, because this is one area where I, I think you guys are at the forefront of this, and there's a couple things about this.

One is, um, let's first of all talk about where has remote patient monitoring been. Or where have you implemented it, where it's been really effective for? For the Mayo Clinic or the Mayo Clinic platform? Yeah, so there's two ways to answer that question, right? One way is to say, I've got a patient in the home and I'm gonna actually look at longitudinal high velocity data while they're in a home hospitalization, pulse, uh, pulse ox, blood pressure, gluc, cometry, and that kind of thing.

And you can use that to say, when is a patient starting to have an issue? Where do they need an intervention? Right? So that capacity, we've done effectively over this time of covid and we've been able to say, oh, well the patient needs to, their Lasix increased or their oxygen turned up, or that kind of thing, and that works.

But here's what's really interesting. Two weeks ago in Nature Medicine. . We published a randomized controlled trial on the use of using telemetry like your Apple Watch telemetry to be able to predict failing heart pumps, ejection fractions diminishing over time. And so what can we do? Well, again, for the I, I know that not all of your listeners are statisticians, but with an AUC of 0.93.

That's really, really good. Right. Perfect. Is one . We can actually predict your ejection fraction from your Apple watch. Wow. And, and, well, why is that interesting? It's like, okay, you can take an asymptomatic person and look at body parameters to predict future disease. Do that with very high accuracy. And that's really interesting.

Thank you for that example, because that's gonna help me. That's a great example. Point nine three. I mean that's, that's a phenomenal example, but who's looking at the data? Is it a machine that's looking at the data? Is it a call, is it a command center? Because it, it can't be a physician who has, or, or even an office staff that has normal kind of functions.

How are we gonna take that data in and act on it? Sure. And of course, this is a brilliant question and well, I have no conflicts of interest, and I am always exceedingly careful about disclosure. Mayo Clinic developed all these algorithms and spun out a company called Anana, located in Boston. To provide the algorithms to the industry to do this in an automated way.

And so as you point out, it has to be a combination of automation looking at millions of incoming signals, and for most of them are just completely normal. And if there is something aberrant, then you'd get a human review. And Mayo does have a command center, by the way, for the review of not only the remote patient monitoring activity, but also for incoming tele.

So, so in fact, to your point about what hospitals of the future have to be, you're gonna be these massive diagnostic centers using humans plus AI to look at all these signals and be able to identify disease more rapidly. You'll find, by the way, in the Eagle trial that we published in Nature Medicine, the algorithm plus human was 30% better.

Human. Uh, that doesn't surprise me. You spent a lot of time in this book giving data studies feedback, and you're fair on both sides. You'll point out studies that one direction and studies that show another and you about the, of, of the different studies, but. I would imagine that's just the, the nature of the world that we live in.

We, we can take AI and throw it at whatever, we could throw it, information security, and we could throw it at AR and AP and those kinds of things and not worry too much. But when we bring it into the clinical setting, we're gonna have to have these conversations of, of what's actually. And what's actually behind the curtain?

What is the algorithm? How did you come up with the algorithm? How's it making the decisions? Because now they're a part of the clinical decision making process and in order to get trust, we need that transparency, don't we? You are so right. So I could say this, that as we use more and more AI and medicine, that we are gonna have a credibility problem.

And that's because, well, at the moment, the FDA through software as a medical device will say that an algorithm is safe. Well, that's good, safe, but doesn't do anything , does it actually work for you? Right. The DA isn't actually measuring that. So what I have proposed, and I think you'll probably see in the upcoming weeks.

Potential announcements about this. You've talked about all these various coalitions, collaborations, joint ventures and consortia that I have Co-ed. We are putting a national coalition together for the standards around evaluating AI efficacy. AI appropriateness, AI ethics and AI bias. And what is gonna come out of this is a nutrition label.

And you say, what? What do you mean a nutrition label? Well, you buy a soup can and it, I'm gonna just guess you could decide if you picked up a can of soup and it said a thousand milligrams of sodium. 59 grams of fat, 500 calories a serving, you might say, nah, . But what if no soup can in the aisle had any nutrition labels and you would just pick a can 'cause it looked good at a picture.

Yeah, but that would be awful. Well, welcome to the world of AI algorithms. No one tells you today what its ingredients were, how, how effective it was, and so this nutrition label will include race, ethnicity will include age and gender. We'll show the algorithm the approach that was used, convolutional, neural network, whatever, AUC stratified by the various populations.

You can say, oh. Well, hey, this one has 0.93 and oh, it's like 60% male, 40% female. It's got mixtures of white, black, Hispanic. Yeah, I think I'll, I'll try this one. Yeah, and it almost has to be done. It has to be done at that level. When, when I talked to some of the smaller health systems and they're like, yeah, we just bought some AI tool.

What does it, how does it.

To be honest with you, the smaller organizations, even if they have a governance group that's looking at it, I'm not even sure they know what questions to ask. It's not like they're an academic medical center with resources and, and or Stanford where they could tap into the, Hey, come on over and tell us what you know and, and help us to understand this.

I mean, a lot of these smaller organizations rely on consortium like that to, to provide some guidance. Otherwise, it's, it's really shooting, shooting in the dark. Exactly right. And so this will be a nonprofit benefit to society, hegemony of none, just like all these other coalitions of academia and industry where we'll, of course bring in collaborators from government, uh, when the time is right.

Uh. I wanna give you kudos on chapter five. I don't know if that was your chapter. I don't know how you write your books together with, uh, but I don't know if that was your chapter or his chapter, but it, it explains the exploring the artificial intelligence machine learning toolbox. It's probably the most.

Clearly understood layout of all the different tools in the toolbox. I've, I've read kudos and if, if people wanna pick up the book, it's, it's worth it, uh, just for that chapter to understand where we are going and what some of these tools are. And I'm not gonna, I'm not gonna go into some of these tools 'cause to be honest with you, I was really fascinated by this, but I'm not sure our listeners would be as fascinated with all the details of it.

I wish. But what you hope the listeners come away with is AI isn't magic. Yes. AI is not a computer that's thinking, right. It's not sentient. It's probability, it's statistics, . And you have to be careful because it doesn't imply causality. Right? And so here's a quick example for you. My mom, when she was growing up said, did you know I heard that ice cream causes polio?

What do you mean ice cream causes polio? Well, all of us would see our friends get polio during the same months of the year they were eating ice cream. Well, it turned out polio was transmitted by respiratory droplets and people went swimming together in the summer and there was water and breathing, et cetera.

So if you ran an AI algorithm and you didn't choose your variables well, and it was just like months of the year. Polio cases and ice cream sales. You would get a really good model. It's meaningless. However, . Yeah. You make that point. It's math, it's data science, it's all, it's, it's, it's not magic. It's, we talk about the, the s behind the curtain, but at the end of the day, that's how we think about it.

But it's not actually what's happening. It's just really smart. Uh, mathematicians, statisticians and data scientists who are putting together algorithms based on the data they have, the data that's available to them.

Yeah. And so it's gonna be a wonderful future, but only if it's regulated and analyzed and transparent. And that's the next adventure you'll see in the next few months. We'll move forward with getting that in place. Alright, so I, I wanna go back to a couple things. So you were a part of the Covid 19 Healthcare Coalition.

You just don't see that in most exemplars in our history. And now, as you'll see in the next few weeks, the vaccine, uh. Credential, which I'm gonna call validated clinical information or verified clinical information, right? We don't like to use the word passport, right? There's a lot of controversy about what it means to be able to display such a credential.

Let's just say this, you wanna go to a sports game, you wanna go to a a concert, and you choose totally up to you that to get into a certain section in the concert, you're gonna just show something about your immunity. It's up to you, right? Do it or don't. You'd want a standard to do it. As of today, 598 companies have come together and adopt that standard.

You might have seen that yesterday, Walmart and Sam's Club announced that every vaccine they give comes with a digital credential that follows that smart health card standard. That vci.org has promulgated. John, I have, I have the really cool looking card. I don't have it on my desk. I think it's, I think it's in my wallet.

But, uh, I have card. I went to the Department of Health. I didn't even go to a health system. It was one of those drive through events, department of Health. And when I was getting it, I said, is this gonna go into my medical record in some way? And the guy just looked at me and sort of smiled and he's just like, no, probably not.

But he goes, here's your card. How does that turn into something that's digital? Let me tell you the two realities of that. 'cause it's all good news. So do you know that every state has an immunization information system? And whether you got your vaccine at a Walmart, ACVS, a Walgreens, a mass vaccination site, or a doctor's office, they all end up in your state's.

IIS the Immunization Information Registry. So what you're gonna see in the next few days, literally a few days, states will begin to announce they're making available to every citizen in their state, a vaccine credential that draws on the state's IIS. So, yes, it exists in digital form. Now, just give you a sort of quick view of Mayo.

Mayo is in five states. Do you know that we have 98% of the vaccination records of our patients, purely because either we're getting it from a doctor's office or another hospital site or something, or a state. IIS. So actually, behind the scenes, the interoperability is better than you think it is. No, that's fantastic.

My follow on question to that is we have actually planned a family vacation next year overseas. So am I gonna be able to flash that at the airport and then flash that in where whatever country I'm going to. Complicated answer. So when you actually look, you'll see that clear. For example, as one of the providers has chosen to use the vaccine credential initiative, smart Healthcare Standard, many of the airlines have.

But in Europe, for example, the World Health Organization has proposed a a different kind of credential. So some of the work to be done still working on this, is to bring the World Health Organization and everything that's been done by VCI together. 'cause we don't want two standards. We want interoperability and crosswalk so that you can do exactly what you say.

So John, before we get to, uh, and I wanna talk about the farm a little bit and that, that kind stuff. But before we get there, is there anything else that I, I, I've started to close my interviews with this question, which is, is there anything I didn't ask you that I shoulda, or that you think that the community would benefit from a conversation around?

You may know that every time I chat with you, I always end on a privacy note, which is that if we're dealing with more data for more devices used for more purposes, we have to get consent, privacy, and security, right? So a couple of the things that we've done, so Mayo hosted a conference on April 22nd, brought together 80 experts from around the world to look at the next generation of consent.

And so what if I said, Hey, bill, I'd like to use your medications, your problems in a de-identified, aggregated way for an AI model that's gonna help people get cures. You'd say, oh, well that's, that's, that sounds okay. I'd also like your genome. Oh God, no. I, I'd rather not have my genome part of that. It's fine.

Right? You should be able to decide how your data is used for what purpose? With some level of granularity. Not insane granularity. I mean, it has to be finite. And so coming out that conference, we have a consent. We've proposed five levels of granularity. So you can see Mayo start to pilot. A new kind of consent about data use that gives patients choice.

That's really fascinating. I mean, 'cause you didn't hear my rant, but I had a rant on one of the groups that has come together and huge anonymized de-identified data source and they're, they're gonna use it for the good of mankind. Well, my data's in there and my rant was, is anyone gonna ask me? I, I, you know what I, I'll probably give it, I'll probably allow it, but I just wanna be asked.

So our view is you need consent. You need DID algorithms that are really good at stripping out the things like rare diseases, things that would be easy to re-identify, and then you still need control of the data. So I'm asked every day, oh, can you ship, uh, the de-identified data outside Mayo? The answer is no.

I'll invite you to bring your algorithm into a secure computing enclave where you can run the algorithm against the DID data and get a result. But no, I'm not going to give the data to some third party. So there's all these privacy and security things you'll see in the next several quarters huge numbers of new hardware and software approaches to keep data safe and to bring algorithms and data together in a way that's privacy, preserving, and ethical.

Yeah. I guess I'm going past, my last question are, are you concerned with the amount of ransomware events that are going on right now? Oh, of course. Right? And so whenever I lecture about security, I describe it as a cold war, right? Everyone innovates to say, oh look, we've put up the world's best encryption.

We got a moat, we got hot oil, we got people shooting arrows. And then what do you know? The hackers invent the missile , right? And uh, it this Cold war, a constant escalation. And it's not at all a project. It is an ongoing process. Yeah. And we need to innovate every single day. Yeah. They're not even inventing missiles anymore.

They just, they keep using the, here's an email, click on this. And that seems to keep working. I, I'd love to see us get really good at defense around fishing and things. Seems they're still starting at that very. Easy level, not that the other attacks don't exist on medical devices and other things that we need to be concerned about, but that it, it just seems that that's the entry point at this point.

You got it. All right, so tell us about the farm. What's new on the farm? Oh, well of course it's now that lovely time of year, 70 degrees in Boston. We've had surprisingly, a good amount of rain. So as the west coast is parched, the east coast, the moment is not in drought, and so animals are really quite happy when it's 70 and moist.

Uh uh. So yeah, the usual part of running a farm is that I'm constantly dealing with illness, disability, right? As we are getting animals that have been injured or abandoned or abused. . So every day there are new adventures in dealing with, oh, this is an infected wound, or this is an orthopedic injury you didn't expect.

Today I have a duck that clearly has a broken neck, but yet no spinal cord damage. Uh, and so you know, we take all of our emergency medicine skills and all the community's expertise, bring these animals back to health the best we can. So you have the largest animal sanctuary in New England. So I mean, are, are you trained in veterinary medicine or do you rely on others from the outside as well?

Well, and so it's a triage as you can imagine, mechanism, which is a, uh, it turns out emergency medicine trains you in a lot of things, right? So the physicality of closing bill's wound, or ripley, the horse's wound turns out to be fairly similar. Yeah, the medications you'd use for you getting conjunctivitis and a goose with conjunctivitis are the same.

Right, so, so we can do that. But then there are certainly specialty surgeries. I am not licensed nor qualified to do. And so we have Tufts Veterinary School just 20 minutes away, and so we can rely on a series of visiting vets for each species that we care for. And so it's a sanctuary. It's not a place where people are coming and doing tours and that kind of stuff, right?

Yes. We actually have tours every day. Oh, do you really? Yeah, well we, and we ran an event last Saturday with 400 people, and uh, the way we did it is we socially distanced. We brought them in for two hours of intense touring and lectures and animal experiences. And then the next group, and then the next group.

And the next group. So. On average, we're running about three tours. Alright. Are you gonna do anything around the health conference when it's up there in Boston? That's a thought. Yeah. No one has asked yet, but because I have been a speaker at Health for a while, they always do these caricatures. So this is the John Halamka mask

So.

Well, remember, both are requiring vaccination credentials, right? Well, well, that's what I'm wondering. So I, I mean, I've been vaccinated and they're gonna require credentials to get in, so everybody there will be vaccinated. In theory, you wouldn't need masks and social distancing. Right? And I think the answer is they're gonna say, indoors, do what you feel is prudent.

And so you'll find some people masked, some people socially distanced, but everyone OnPrem will be vaccinated, including staff. Fantastic. Hey John. Thank you very much for your contributions to the industry as well as. For spending some time with us. We really appreciate it. Well, hey Bill, always happy to talk anytime and I'll close with one sort of silly thing.

So while we were talking, a three-year-old just ate that. What are you gonna do ? Three mushrooms? The answer is it's marasmus orient. I recommend garlic, a little onions, and. Maybe som olive oil . So you get pictures of mushrooms all day. That's, that's, that's what people are sending. Wow. Exactly. , this three year old will be fine.

That's, that's well, and actually that's great. Peace of mind. I, anyway, I, I'm an example of, I had a phone call with an emergency me medical doctor, and it was in the middle of the night and I.

Or we went back and forth in text. We had a phone conversation and he said, you don't need to go to the ed. And he was right. I didn't need to go to the ED based on things. That's the, that's the value of, of telemedicine. It's, it's really putting me at ease, putting my mind at ease. I, you know, 'cause I don't know, at that point I just feel pain.

He's like, no, that that will pass. You'll be okay. I'm like, okay. That's all I need to hear. So see, this is the new normal. It is. It's new normal. John, thanks for your time again. What a great discussion. If you know of someone that might benefit from our channel from these kinds of discussions, please forward them a note.

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