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The 229 Podcast: Exploring How Epic Cosmos is Changing Care with Phil Lindemann

Phil Lindemann: how can I instantly learn from what the million other physicians have done in similar situations and get the insights on that, right, in my workflow.

Bill Russell: My name is Bill Russell. I'm a former health system, CIO, and creator of this Week Health, where our mission is to transform healthcare one connection at a time. Welcome to the 2 29 Podcast where we continue the conversations happening at our events with the leaders who are shaping healthcare.

Let's jump into today's conversation.

All right. It's the 2 29 podcast and today we are joined by Phil Lindeman, VP of Data and Research at Epic. And recently returning from the Packers Second Victory up at Lambo Field. Congratulations on that victory, by the way.

Phil Lindemann: Thank you. Hard earned. Yes.

Phil Lindemann: is pretty hard. Like, I got to bring my daughter who. knew very little about football, but I was like, this is an experience that's like going to a rock concert.

And she, I, she loved it. It was great. All like, Vince barely slept. It was awesome.

Bill Russell: Well, how old is she, you think? You

Phil Lindemann: think she's she's 12 and I'm not gonna say like. The rules of football were, we were pretty rudimentary when we were when we got there. There's offense, there's defense.

We started there,

Bill Russell: but it's yeah, but it's every father's dream to, you know, be going to games for the next let's use 12, you know. She comes home from college and says, dad, can we go to a game that's, gonna get in trouble for that, by the way. because one of my daughters actually is my producer.

When you started, did you start right outta college? Oh,

Phil Lindemann: yes. I had never heard of the place. They hired me and I thought, this is how old this was. I thought I was gonna take CDs and put them in computers at a hospital, and I was gonna go from computer to computer and just run and maybe set up a couple settings.

And I was like, I could do this

Bill Russell: what does the progression look like at Epic? We are gonna, we're gonna dive into Cosmos in a deep way. Sure. But what does the progression look like? You start as a college student and you know. Very little. I mean, as a college graduate, you're okay, I know a computer, I have some skills.

And Epic sort of takes you under their wing and says, all right, you're gonna, you're gonna learn healthcare a little bit at a time here.

And it turns out it was the largest go live of multiple products or different departmental things that had ever gone live in history. And to me, I came in here thinking, ah, they've got this all figured out. It's software. There's no bugs. Everything must work perfectly together. So it was like this really rude awakening, but it also was an endless amount of problems to solve.

And to solve the problem you first had to understand the clinical context or the workflow behind it, and I just, I loved it So. Kind of things took off from there.

Bill Russell: the transition to analytics. So you work in a lot of different let's just call them modules within the, right. The, The hospital system.

And then you you kept complaining about the data and you got that phone call from Judy talk, talk a little bit about that.

And as I went from module to module, as you're saying, I said, why is it so hard to get a report? Like I just want a report that tells me the most of this thing happened. It's still like seemingly simple things, and it would be really difficult for me. And then one day. I got this call and says, Hey, you're gonna go over to analytics and you're gonna figure out who was reporting.

It was called at that time. And then I was like, oh my gosh, this is so difficult. This is so hard. And it wasn't a technical thing necessarily. Some was technical, some was just administrative. So it. It really, I think I have this unique perspective of, I know how all these products feel about like you have to make the software work for your users and that's like top priority.

And then the analytics team is sitting there saying, we're gonna build a platform so we can build all these reports for everybody. And I can sort of see the pain of both of those worlds and help make them work together better. And. I think that will probably never be done, Lisa. It's like flossing your teeth.

Bill Russell: So it's a never ending process. Yes,

Bill Russell: Former CIOI was never an epic customer but I will tell you, doing the best of breed thing, data and analytics.

It's like bringing that data, build out a clinically integrated network, and I'd look at them and be like. There's 45 different EHRs, like, yeah. And we're gonna measure the clinically integrated network on this standard set of, you know, things. I'm like, yeah, but we don't collect the data the same way.

Like you have to start at such a rudimentary level, like we're gonna, we're gonna collect the data this way. And then you look at the data structure and how EHR stores the data and it gets. Messy, and then you have to bring it in, and then you have to clean it up, and then you have to I, this sort of leads me to Cosmos and saying, yeah this was, no, I mean, people just keep throwing this number around like it's no big deal.

Where I'm watching at UGM, where the doctors essentially using Cosmos as a as a tool, as a almost another set of eyes on some of the work that they're doing.

Phil Lindemann: Yeah, it's how can I instantly learn from what the million other physicians have done in similar situations and get the insights on that, right, in my workflow.

So it's it's like the point, it's what we've always dreamed that this would happen. So even when I started 21 years ago, you know, this idea that we could learn from each other if all of this data could be uniform and mapped together. So I think. You're right. It's just there was a lot of hard work that led to this, and now you're sort of seeing the fruits of it and it's it's assumed that it was obvious like this.

I mean, it's all the same stuff that you'd see at any analytics discussion. Like I've been at industry conferences and like, you know, capital One and Delta Airlines have the same problems. Like it's just there. There is a similar theme to how these things are solved here. So yeah, building Cosmos was not an overnight process.

And,

I think some people assume that it's just, oh, yeah, Cosmos, click the button and boom, all the data's in there, and now we're participating.

Phil Lindemann: Yeah, well, you kind of have to think about the work that was done leading up to it. It's like everyone has stood on the shoulders of the previous person, so now we're all on the hundredth floor thinking, oh, this is easy.

But it was like foundationally, a whole bunch of stuff had to be built before, but like if a new group signs up and we do them in waves, there's like four waves a year where. A dozen groups will go live together and there's an eight week onboarding. Doesn't take that much time and they map a couple things, but it's, it is relatively straightforward for a new group to add now.

So, you know, we've got a decade of organizations that have sort of started cleanly and you know, some of the older ones we work through that, but it. It's, it is it is a challenge.

Bill Russell: So I'm sitting at UGM I'm watching. Yeah. You know, you guys do the, I don't know what you call that, sort of the role playing it's called

Phil Lindemann: The skit.

Yeah, the skit. It's a fun thing.

Bill Russell: So you're doing the skit and I'm watching as the doctors having a conversation and Cosmos is essentially saying, Hey, consider this. Look at this or those kinds of things. Which is really the promise of ai. AI is the assistant that sits there and says, Hey I've looked through the clinical documentation, I've looked through other patients, I've looked through all these things, and responds back and this is it feels like we're just scratching the surface.

And they both looked at me and said, no, you're right. That's. That's a, an amazingly powerful tool. And you're right, adoption is gonna be, is gonna be the challenge, but just to have that tool is very powerful from their perspective.

Phil Lindemann: Yeah. John Lee, I don't know if you know him, he's a physician.

I'm sure you've run across him, but you know, he, this was years ago, he said, you know, Phil, someday. Patients are gonna start asking. He's like, they already asked this question. Do they have MyChart? Does my doctor have MyChart? And he said, then they're gonna start asking, does my doctor have Cosmos? And you're right about that.

And you can even, you know, we have this sort of. Moment where the physician can share that right in the exam room with the patient and explain it to them and say, you know, we have a couple options for medications here. And really the physician said, you know, what's cool about this is, you know, patients ask you all the time, well, what worked for patients like me?

Like, what do you think will work? And now it's getting at the point where you could say, well, it worked for patients like me.

So you have the medical record. Is it based on outcomes? Like, do we have that kind of data in Cosmos where it's saying, look, this was effective?

Phil Lindemann: That's the whole point, is. How do you say something is better than something else if you can't measure it? So when we look at something like high blood pressure, we obviously measure blood pressure.

It's a pretty common variable, but really the reason why you don't want to have high blood pressure is so that you don't have a heart attack or you don't have a stroke. So those are the endpoints that we're looking at. And because Cosmos has these longitudinal records, in some cases, over 20 years of longitudinal data on a single patient.

Well, we know, okay, that one, there's no heart attack. So that's the types of things that we have to think through that are sort of like under the covers, but to the physician, ideally, it's a very clean, straightforward experience That's.\ Essentially crowdsourced by their peers and saying, this is what has worked.

It's not what was the most popular drug at the time, or which one was ordered the most? It's outcomes driven. It is actually looking at which drugs work the best which interventions or treatments work the best independent of how many times it was ordered or how many times it was given. then because of the size of Cosmos, which I should, it grew a little bit.

It's 300 million not two 50, but whatever. Well,

Bill Russell: and by the time this airs, it could be up to three 20. Sure.

Phil Lindemann: But anyway, so it's using that as the cohort to it.

Phil Lindemann: I think of Cosmos almost as, it's almost like a living entity. It is the community that participates in it. The thousands of researchers that have access to Cosmos, and then it's this whole set of tools. That we build on top of it that essentially poke into the medical record.

So the one I just talked about best care choices is one we, something very simple. We do growth charts. So, you know, you look at your kids' growth charts, well, there are children with rare diseases. That affect their growth. And oftentimes there is not enough data or the growth chart is very old that exists.

What happened to patients like me and sort of infusing that throughout the medical record. So when we designed Cosmos, that was really the North Star is how can we box up the learnings, the insights, and then just make that integrated into the fabric of Epic so that every doctor can benefit researchers benefit.

I love to talk about the research side of it, but. The focus is how do we make the physician and patient experience better, which I think you saw a lot of at UGM on stage.

Bill Russell: I was gonna go into the clinical trials and the research perspective. Talk about how it's being utilized, like what types of organizations are utilizing it, what kind of research are they doing and what kind of outcomes are we seeing?

So it, it's rewriting the first line in a lot of research for every one of these rare diseases that someone wants to do. So those are kind of quick wins. You could go and do a paper on that. But right now there is. Something like 2000 plus researchers that have access to Cosmos all around the world.

500 of them are data scientists, so they're actually writing code in Cosmos and having access to that. And right now we're seeing about two peer reviewed journals coming outta Cosmos a week that the community is building. So those are other health systems that are in there actively doing research.

And one of the neat things that I like the most about it is. Cosmos isn't just what meds did you have and what diagnoses? There's social drivers, data, how much do you drink? Do you smoke? Do you have transportation issues other information that gives us a more holistic picture of a patient's health.

So I would say research, I don't wanna say it's booming, but it is, it's like the rocket ship has launched in the last year as we're watching what the community's doing. And it's just great to see because it, it was a trickle at first, but now. Community has published more journal articles this year than they have every year combined of Cosmos.

And that was by August 1st, so that was a little bit of delayed data. So the research community is really strong in Cosmos and just getting stronger.

Bill Russell: I mean, do you guys bring that community in and of itself together, sort of like you bring

Phil Lindemann: the providers? We do. We there's two ways we do it.

What statistical libraries do you need? Do you need more GPUs? What types of things do you wanna do? But then the most fun is, and this is a core tenet of Epic, is if you wanna learn. How your users are gonna use your software. You gotta go observe them. You have to go immerse yourself in their environment.

And in research, we need to go immerse ourselves with researchers. So we have data hons all over the country where our research and development teams, our researchers, will go out. And hang out, you know, in a large cafeteria or an auditorium. And everyone will have their laptops and have their they're logged into Cosmos and they're just ripping through queries and trying to get ideas for papers.

So make sure it's all mapped. Let's make sure we have access provisioned. Essentially with Cosmos, they just say, I want that person to have access. They're eligible for X, Y, and Z, and away they go and they've, they're off dipping into a large data set with a whole set of statistical libraries to apply to it.

So it's, it's something we've all wanted for a really long time.

Bill Russell: I mean, it would be Christmas for a data scientist. I mean, exactly the first time they get in there, they're just like, oh my gosh. Look at I've, right. I have access to. Is there a data set that you sort of covet that you look at and say, man, if we could bring that data set into this, or do people like just take Cosmos as one of the data sets and then go out and get a whole bunch of others?

Phil Lindemann: Well, so, because Cosmos is de-identified. You can't bring more data into it, sort of, you can't side load data because it would redo the de-identification algorithm and we can't do that. But what's important to know is, you were talking, you know, in the old days, how did we bring all these systems together?

So that's the data set that I want is we've only brought a portion of that together, a high value portion, but there is so much more work to do in the depths of very specific subspecialties and different social driver data that we can continue to bring in. So. There's a never ending stream of updated data types that are getting into Cosmos.

And sometimes we do fun things like

Bill Russell: you just wanna get all the epic data

Phil Lindemann: into Cosmos. Well, let's start with that. That is the easiest one to normalize and bring together.

Bill Russell: I was kind of surprised that the social determinants data, but if I thought about it, health systems have been collecting this for quite some time.

I just

Off social driver data, you have to have social driver data. So we gotta start collecting it now and we have to make sure it's embedded into Epic in a meaningful way that everyone's not sort of just inventing this themselves. So we looked at what was then Institute of Medicine's. 10 sort of drivers of health, social drivers of health, and then modeled it after that.

And we've since expanded it. But it meant that everyone in the world who wanted to collect this data was collecting it in the same format, mapped to SNOMED concepts, if that, you know, for a couple listeners who are gonna speak ontologies. But that was a core part of it. And then monitoring that, it's being used, getting implementation programs to say, how do you get to talk to a patient about these things?

Bill Russell: Yeah. I was listening to the acquired pod podcasts on Epic, which has made its round. I've talked to a lot of people that have listened to it And somebody from outside the industry was saying, you know, what's the most impressive thing about Epic? And I said, you know, people are gonna talk about a lot of different things, but for me it's one thing that's been applied differently over the years and that one thing is it used to be really hard to get people within the health system to agree on things.

And Epic came in with a very prescriptive model and said, Hey, this is what it takes to do a successful EHR implementation. And they got everyone sort of working together. And while they did that in a hospital, which as a former CIO, I know how hard that is. Yeah. Especially as a third party, like as a third party, they were able to.

We're not gonna force you to share your data, but if you do share your data, your doctors get access to this and your researchers get access to this. And it incents the behavior that as a patient we all want. And I don't know, you can't really comment on that, but I just sort of wanna throw that out.

I mean, it's just one of the, one of the impressive things. I mean, Cosmos is very hard to do as a single system. And it takes it takes someone at that at Core. I want to talk to you about ai so, a lot's been said about AI and Epic and UGM, the awful lot of announcements about AI and there was like 160 some odd things last year.

What does it feel like to be a practicing clinician in the next couple of years with AI sort of coming along?

Phil Lindemann: there's a lot to, to unwrap there. You know, I think as we're looking at how do we implement AI around the company, part of it is getting people to rethink how they approach a problem. There were things that were just, I don't wanna say they're too hard, but they were not worth the squeeze to optimize versus just.

Like when I was talking about the growth charts before in Cosmos. That was something somebody asked for 10 years ago and said, oh, if we could just bring data together from our whole community, we could build growth charts for rare disease. And then literally like someone just forwards me this thing from 10 years ago and says, Hey, can we do this now?

And I was like yes we can. And sent it off to our RD team and in six months they had the thing in production. So. It's some of those things where we have to reevaluate where we can have the biggest impact. Now that we know AI is one of these tools in our tool belt.

Bill Russell: Are your coders, I would assume are using AI pretty extensively at this point.

They're not hand coding as much as they used to. Oh,

Phil Lindemann: absolutely. Yes. Yes, it is essential if you want to,

Bill Russell: so when we see more things being released as the, is the velocity higher?

So we have to sort of take an approach of can we get it as a feature that can be turned on with zero implementation, where it's just obvious like, okay, that's how that should have worked or is it worth the cost of what it's going to? The cost of what it's gonna take to implement this. So some things you're going to have to explain to a doctor, or they're going to have to, you know, do a little bit of education on it, or it's something that they're only gonna use in certain situations.

So we have to be thoughtful about when we roll something out, what is an obvious win? Like, this is just gonna be a great thing. It's gonna work every time. And that's a home run, that's, let's do that versus things where it's like, well that'll be kind of interesting in a couple situations, but.

You know, maybe we don't spend our time on that.

Bill Russell: What about your old stomping grounds? The analytics side? I mean, is AI transforming how we interact with the data?

It is. Like, I think SQL Report writing in a few years I think will be sort of this you know, classic cathartic experiment that people do for fun. Like I,

Bill Russell: so you think we're gonna be looking at the enterprise, the Star Trek, like talk to the computer and it's going to write the SQL queries, write the R, whatever, and it's all of a sudden it's just gonna pop back some data for us.

Phil Lindemann: That. So yes, it will get there. What the timeline is, I think is what everyone will debate. The problem is literacy still exists, right? That is the big issue, is like we have tools. Right now we have this one called Sidekick, and Sidekick is a way to ask questions of the data. Not as a lay person, but like basically as a departmental expert.

How we can make people more literate about how data's going to give you answers. It's like when you ask a report writer for something they know, like what you're kind of getting at. How do we turn that into something that can be codified? And until we get there, there's no magic box that's spitting out answers.

The literacy gap is too big. So it's interesting because we have made tools that are for end users, like, who don't have a lot of data literacy that are sort of almost having queries written for them. And what I'm finding is. That's good. It'll work for something like show me the top 10 times things this happen.

But that's where I'm, there's like this duality of, we're always trying in analytics. To push things from the analytics department to the frontline, like ideally, an end user who has the question can just get the answer with no interventions in between. I don't see that. As it's gonna change overnight until we close that literacy gap.

Bill Russell: I was talking to a CIO I'm sort of cheating here a little bit, but I'm bringing, like, the conversations I've had just this week I was talking to a CIO and he was saying, you know, they're doing really cool things with AI for the for the clinicians. They're doing really cool things for the patient.

I mean, there's some good announcements there. On the analytics side and the researchers, he's like, you know, but I've got this team that's just inundated with stuff. I'm like, can they make my analysts and my other people, my builders, what can they give them a set of tools that's gonna make them more effective?

Phil Lindemann: yeah, nonstop. There's an entire team working on those types of initiatives. So there is you know, in, in analytics, when you think about how people do things, they say, well, is there a report for this?

They kind of do a search, right? Is there a report? And if they don't find a report, well, can I build a report with like a report building tool? Okay, I can't build a report. Can someone code me a report? So there's like this continuum of complexity and we've put an agent on top of that essentially you can ask a question to and it'll say, oh, is there a report already made?

Can I make one? Or can I go write one for this user? And building that tool is something that we think will supercharge analytics teams who we're gonna go through that process anyway and try and do that. So that's. On the analytics front for the analysts, we're also looking at ways that build can be automated in a similar way.

In some cases I'm just using configuration standardly in Epic. But sometimes those things could be easier to say, okay, build me a rule that says, well, this and this is true. So we're building an AI that you can ask at a question and it will start to draft a rule for you that you can basically have like logic being built for you, and then you can plug those logic into standard places in the system.

So a little complex, but yeah, there's the stuff where it's like the analyst knows exactly what they wanna do. Can we just have it talk to the computer? Computer does it. You okay it. You bring it to change control and away it goes.

Bill Russell: So I was after UGMI was I wrote a couple of articles and I monitored the various social media things.

I'm curious with those two things as the backdrop of people's concerns, I'm sure you get asked about those things all the time. I mean, what's the

Phil Lindemann: Yeah.

Bill Russell: What's the response?

Phil Lindemann: Yeah, and I think the, from the privacy, I think some people look at us and they're like. They're almost a little perplexed because usually when people are trying to bring data together, it's to sell it, right?

They're trying to build something that's an asset. Since day one of building Cosmos, the Cosmos data can't be sold. It can't be sold by Epic. It can't be sold by any member of it. So that immediately. Provides a safer haven that people feel comfortable knowing that the only place their data will ever exist is within the confines of Cosmos.

Get both sites and patients and things like that on board with is we did things like support, local laws and rules. So we went above and beyond HIPAA and standard privacy laws. So like New York State, HIV results can't be transmitted to these, to something like this that's just in their laws.

So we had to be able to support those types of things. And then we had to add things like a patient level opt out. So patient doesn't wanna be in Cosmos. If they can let their doctor know and they'll be out. So we wanted to make sure that the site had control but we didn't sort of. Tear down the data so much that it was unusable.

Bill Russell: The fact that the data can't be sold and it's not intended to be sold. I think that always throws people for a loop. It's like, why are they doing it? It definitely

Phil Lindemann: does.

Bill Russell: Yeah. Like, yeah. Yeah, because there, there are epic customers that have formed their own consortium to put that data together for the purpose of selling it.

I mean, yeah. But, and that's, you know, as long as they follow those rules it's acceptable.

Phil Lindemann: Yeah. That's their prerogative. I mean, it's something that's gone on for decades and there's hundreds of businesses that do things like that. And everyone has to differentiate somehow. Ours was to say, our goal is to build tools that are gonna help the doctors that made the data in the first place.

So it's really, we see it as the closed loop system. So the value for Epic is we're gonna make the thing we make for doctors better.

Bill Russell: I'm an optimist. I'm a technology optimist. I believe that technology is gonna make healthcare better, but you're gonna give me some ammunition here. So, alright.

But with that being said, when do you think we will start to see some material improvement just based on the utilization of ai, the utilization of data in those things? I thought it was interesting this year. For the first time in I know this is pharmaceutical, but the first time in decades, we saw the obesity rate go down and it's GLP ones, right?

So it's just like this one thing transformed a. A trend that had been going on for decades. I'm curious when we can maybe anticipate data having that same thing, maybe new outcomes, new advances, new or maybe we're already seeing it. I don't know.

But I think our own research department, epic Research has done. Some studies on lifespan of patients with like cystic fibrosis and different things, and shown how it just continues to go up and up until it almost becomes the standard for human life. So I think within certain diseases you see incredible progress that is going to be in the data and it's being able to have a data set that can focus in on those microcosms If I have to sort of, what's the.

The call to action here. I think we as a society need to be better about getting patients to be part of that loop where they're recording pros in their phone or patient reported outcomes. When you're on a brand new drug that's new on the market. Every month they should be asking you how it's going. Are you having side effects?

So I think there is an opportunity for transparency with data like we've never seen before, knowing that every player in the health system now can have a connected device into that one ecosystem. Patients have MyChart, epic users in health systems. There is payers through payer platform.

We're trying to get the life sciences companies on board. We have aura for diagnostic testing companies, so, you know, with the patient at the center and then their care team and clinicians. Our goal is that this whole ecosystem could be communicating with data to make sure the best and the brightest are coming to the top.

Phil Lindemann: Yeah, the next thing. So like, that's just, that was almost a mechanical joining, but the next thing for us is.

As much as we want to have this be true, the way that we've designed the flow of information and work in health systems and things like that doesn't always happen perfectly, right? People write things on notes and they put things here and there, and how do we catch the things that drop in between these handoffs?

It's just subtly there and it might get missed. It gets passed on to a couple different doctors. Now we use ai. To scan that radiology result. And if it sees something that says, oh, they need a follow up, it makes a discreet follow up in our system, that goes to a queue that someone can monitor and it doesn't get lost.

And Christ Hospital, when they implemented this, found that their, I don't remember the exact percentage, but you know, they are finding a higher percentage, like 20% higher. Early stage lung cancer than the rest of the country because they're picking these things up and then calling these patients and having them in.

Like that queue of follow ups already existed, but certain things didn't get put on it. Now AI is helping. Nothing falls through those cracks and puts those things in there. I had a physician tell me there are hundreds of those types of situations in medicine that we're gonna be uncovering those rocks for the next few years.

And just saying, attack that, figure out that handoff build something to help that, where we've already built the substrate, the infrastructure that has the queue and has the reminder and has the alert, but things fall out of that system. How do we pick them up and put them back on the conveyor belt to that system?

That's, to me, that's pretty exciting. Although it's. You know, you're picking up nickels there, but they're nickels that could save someone's life.

Bill Russell: Oh, absolutely. Uh, Last and most important question packer Super Bowl.

Phil Lindemann: Of course every

Bill Russell: year course for every year. doesn't matter what the, it's like, yeah.

Really appreciate it.

Phil Lindemann: Excellent. All right. Great talking

Bill Russell: to you.

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