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 Welcome to this week in Health it where we discuss the news, information and emerging thought with leaders from across the healthcare industry. This is Bill Russell recovering healthcare, c i o, and creator of this week in Health. It a set of podcasts and videos dedicated to training the next generation of health IT leaders.

Well, there's these broad trends that continue year in and year out in the sort of broader landscape change in the payment system that is progressively moving to more value-based care. And that'll take decades to play through. But nonetheless, it moves. And so every year it's kinda, where is it? Where are people trying, et cetera.

So that's one. On the technical side, uh, I think the AI sort of broadly speaking, intelligence and analytics continues to be, will be quite profound. Over time. And so we'll see kind of what progress we've made last year. Engaging consumers remains important, but challenging and we'll look at that. Uh, plus is the, you've probably seen the federal government issued some of their interoperability rules this morning, so I'm sure there'll be a lot of discussion about those and the whole topic of interoperability.

Yeah, so now we. Finally have a definition around what data blocking. Well, I, I haven't certain seen, and you will see as will I, uh, these various, uh, you know, interpretations of this 700 page proposed rule. Right? Uh, and I think best I can understand what it'll be primarily is what blocking is not. Uh, and so from there on out, you begin to, you will field, if you're the federal government complaints and through sort of case law begin to sort of refine that well, and there's seven exemptions.

So it's like, here's the definition, here's the penalties, and here's seven exemptions. Yeah. So we'll see. We'll see. Um, and I'll be honest, I, I read it about an hour ago, so, well, you've got an hour on me. I haven't. I'll wait for the two page summary. Yeah, that's exactly, I read, I read the, uh, a couple of the articles on it, so it'll be interesting.

Yeah. Um, so, uh, social determinants, population health, uh, value-based care. Yeah. Continue to be drivers. How is technology gonna play? I mean, what, what technologies do you think are gonna play and, and how will technology play in that? Well, I think there's a number of ways. If we take the area of population hell, this is all right.

If I need to take care of you in a group of people like you, I need to characterize you, I need to understand you, and I need to understand clinically what you're, what's going on. I also need to understand socially, whether you're poor or not poor, et cetera. I need to understand your genetics. I need to understand a range of things.

So we'll use the technology to gather the data to characterize you and to characterize me. And the point of characterization is say, well now I know the plan. Here's what I ought to do. And the plan's different depending on whether you've got means or you don't have means or diabetes or you don't. So that will, it'll be a lot of collecting of data to help formulate strategies.

And then a lot of the technology will be, uh, follow, well, the strategy's working. Plus we have to introduce into the workflow, both the patient and the clinician. Here's what should happen next. It's interesting 'cause we, um, another one of those cases, . Where the job is becoming less and less about technology and more and more about integrating with the business.

Yeah. We're not even asking some of the, this is what we heard in the last talk. We're not even asking some of those questions yet. Yeah. To build out a, a whole person profile, if you will. Yeah. I mean, we, you know, we talk to, you know, clinicians and say, all right, I'm happy and I get the need to catch social determined about food insecurity.

Okay. But what do you want me to do? You know, if someone says, I have a tough time finding, you know, getting a meal, or I live in a, you know, the only thing that's close to me is a convenience store. Yeah. Uh, and so it is, you know, we are having to work . But Well, what do you do once that? So actually the technology part will be the easiest part is finding out there's food suite.

It's now, no. What will be the more challenging part? Well, I, I mean, technology plays a role, you know, we can, we can Uber people around Sure. And actually get them to where they need to get to. Uh, we can address loneliness, which we heard this morning is a major issue. Through, uh, you know, video visits and video chats and some technology, but the whole idea of, Hey, I live in a place without an air conditioner.

I mean, is the health system supposed to start buying air conditioners? It's, well, I think, you know, one of the, uh, our, our clients we work with, they work encouraging all the elderly people to get out and do their 10,000. Steps and it wasn't happening. The question is why? Because the leash laws weren't enforced.

They were afraid of the dogs. I said, well, son of a gun, how do you fix that? And is that the job of the health system to fix the leash law? And a couple of about a year ago was talking to the guy who was the head of Medicaid for the state of Arizona. I said, what are the two largest social determinants?

And he said, homelessness and incarceration. You know, you come outta jail and you can't get a job. So if you're banner or dignity or any health system, Arizona, what do you want us. To do about the incarceration problem. So we as a society and health have to sort through what is it we expect out of all the various players.

So, um, one of the interesting things in the last talk, she was describing data silos. Yeah. Now, when we talk about data silos, typically we're talking, you know, our E H R data is whatever. When she was talking about data silos, she was saying, Hey, we need, uh, we need housing data, we need education data, right?

We need all that, all those kinds of things. Are we gonna start, uh, creating? Is that gonna be part of our repository? To, I think it's sort of what she was pointing to, that if you really want to address the complete person, you know, you're gonna have to start looking at all this stuff. And, and it's not just, and sometimes when we talk about ref, you know, the interoperability of exchanging clinical data, but if I refer you to a place for food security, there's an interoperability loop.

Presumably there's a referral coming out and whether you took advantage from the back. So I think it will cause us to look at a broader, more multifaceted, more complex meaning of interoperability and what we have to do and the collecting of data to go with that. So like a . Like a homeless shelter. We would, we would have a record of this person coming to a homeless shelter or a food bank.

I mean, that's part of the theory. So if you showed up next appointment, I'd be in a position to say, how come you didn't take advantage of the food pantry? You know, what's going on here? Do you need, do you need other help, et cetera. Is that the physician or is that gonna be, I think, you know, you point all these things, I, you know, you, you can argue probably appropriately so that it's someone in the office who's pre-screening you and says, by the way, I know you're going to see Dr.

Yeah. And I'm like, okay, well our medical group data is still not talking to our acute data. Right. And now you're adding in, you know, long term acute, I mean, just all these different, uh, clinics and whatnot around the thing. And now what we're saying is that that continuum of care is now expanded. Correct.

Well beyond that, you know, our knowledge of what it takes to be healthy, you know, and we get into this stuff that says, uh, are you lonely? And what do you want me to do about you being lonely in a variety of things like that. So it is not just the what I can get food or feel safe at home, it's just kind of, am I part of a community?

Am I engaged? Am I feeling valued? So if you were a C I O today, what would you be focusing in on? Well, I think, you know, and that's part of, I think where we could have done a little bit more in the prior talk is you say, golly, it's overwhelming. Where do I even start? You want me to solve incarceration?

How do I do that? Yeah. I say, well, I don't think you gotta do that. I think there are tools that allow you to do a social determinant assessment, prepare tool as one of 'em. So why don't you start doing that, uh, and gathering data about who's coming in, et cetera. And there are, uh, companies that have resourcing in your community.

Here's where you go for domestic violence help. Here's where you go for financial assistance, here's how to get a ride to the practice. And I'd start putting those into the E H R, such as the tool says, we got a food security issue, here's who to refer you to. Basic stuff. Uh, and not, you know, worry right off the bat about how in the world am I gonna connect to this silo or that silo, et cetera.

We may need to do that. We don't have to wait for that to get going on this stuff. Well, is it going into the E H R I? We're going down a Yeah. A very specific, yeah. Or, or is there, is there a layer above that, that you're bringing all this data into? 'cause there's better interoperable. Yeah. Anr. Well, I think it's one of the sort of broader challenge is in a lot of ways you say this is this population health layer that sits on top.

'cause we look at, for example, in our case, our average customer, a health system has in their catchment area, 16 different EHRs. You know, and they don't, they're not in full control, so they're gonna have some interoperability issue. By almost as a matter of course, you'll do this layer on top, brings it all in, cleans it up, associates you, you know, across multiple medical record numbers and you could argue that's where it ought to go, et cetera.

Now that being said, we need to get, if you're in front of your doctor, we want your doctor to be aware of certain things. So there's this, how do I fit it into the workflow? But it may very well be that the right, uh, home for this is a pop health layer or extended h i e layer of some form. Interesting.

Yeah. Um, I mean, what are you gonna . What are you excited about at the conference? Oh, I think, you know, I, in some ways besides seeing your friends, oh, I guess, you know, being, having been in this injury for a hundred years, , uh, no, in some ways I wish I were, uh, 20 years younger, uh, because it is a remarkable time in terms of the potency of the technology.

So part of it's just to see where things are and what are people learning about, uh, you know, uses, you know, it's a classic garner curve, but a lot of this stuff's way up at the height. Yep. You say, well, let's cut through the, the noise, uh, and see the reality of a lot of things. And that's always just kind of cool to see.

Will be, uh, it'll be interesting. It's, it, I, I, I continue to say it's gonna be, it's a great time to be in healthcare. Yeah, sure. And, uh, it feels to me like the pace is increasing, but you've been in at this longer than I have. Yeah. Is the pace increasing? Well, I think in a couple, yes, and in a couple of ways.

One is the, the, um, business model shifts or occurring finally to the value-based care. So that's sort of picking up an urgency. The second is that the pace of technology is accelerating and accelerating in a more potent way. Um, you know, if you look at, I remember, you know, if you take every . Decade and say there's a technology that occurred in that decade that changed the world.

You say, well, what is that? Well, in the seventies is the mini computer eighties. It was a network personal computer. It was nineties, it was the web. In the twenties it was the mobile device. And in this uh, decade, it's AI and all those pick up and they all come gang up on each other. It's not like we're done with a web or done with mobile and you say, golly, this sort of compounding effect.

Uh, plus you get these new entrants in here like Google and Amazon and everybody, and all of a sudden people say, holy smokes. There's an urgency and an anxiety here, uh, because they're very potent organizations that can move fast with vigor and effectively and will that change the game? So I think there's a, there's a pace change, there's an anxiety change that's different, uh, and that's kind of remarkable.

Yeah. And all the, all the players that are getting in have different objectives. Yeah. I mean, Google's objective is very different than Amazon's, uh, apples and whatnot. They all, and they're all taking a piece. Yeah. C v s Aetna though, that's a little bit. Close. Well, I have a, I teach this course in eHealth at Wharton in the second year MBAs.

And so our course, our class in about a month is we're gonna look at the strategies of Google, Amazon, and, uh, apple, and talk about, well, what do you think is going on here, your class, and do you think they'll be successful and what's in their way of being successful? It'll be interesting to see, you know, what the, what these young, bright people, uh, come up with.

And it's, it's not. It's not a foregone conclusion that they will be successful. Correct. 'cause they've, they have failed in the past, you know, I mean, you're right. They, and you could on one hand say, well, they failed. They don't know nothing. They're gonna get their fingers burned, et cetera. They're smarter, but they're smarter.

Uh, they have more reason to be in here. The it, the times are playing more to their strengths. So you could say, look, the mobility stuff quite real, everybody's real. So Apple's got a real strength here. Plus you say, well, the data and the analytics is much more real now. So Google's got an asset to play with, et cetera.

So I think there are needs in this industry which play much more to their strengths. They have greater reason to be here, et cetera, and they're hiring some good people. It's not like there's devoid of talent. My last question, is there a technology. That is going to help us to clean up the data, right? Yes.

So I mean, that, that seems to be, you know, to take advantage of machine learning and ai. Uh, I, I heard Mayo's Mayo presentation today said, look, we're gonna be in better position to take advantage of machine learning and ai, because they did all that very difficult, uh, work as they went to Epic to really clean up all the processes and whatnot.

But most health systems have 16 EHRs and whatever. Is there a technology that's gonna really help us or is that just gonna be, there's a class. I mean, I think the class of machine and pattern learning will do that. So the machine, you know, for example, in the Healthy Intent, which is the Cerner population health and bring in data from lots of different sources.

Uh, 95% of the cleanup is, is done by the machine. This is, this is the pattern that's going on here. This Mr. Smith is a diabetic. I know it's not in the problem list, but this is what's going on. And in a lot of ways you borrow, uh, technologies and insight from other industries. So if you look at, um, the intelligence and national security.

People, they look at patterns of data coming in and golly, there's an elevated terrorist threat here. Uh, and they're looking at radio and TV and all kinds of stuff. So we're getting, as a, uh, broadly speaking across multiple industries, much better at picking up patterns and say, this is what's going on with a pretty reasonable degree of certainty.

So that will, it doesn't solve everything, and it doesn't mean there aren't kick outs or the machine will get it wrong from time to time, but it's, it's actually quite impressive what could be done. It's exciting. Yeah. John, always a pleasure. Always a pleasure. Thank you for your, uh, pleasure to be here and, uh,

Look forward to hearing all the rest of these, uh, interviews that you've gathered. Should be fun. I'm looking forward. Great. I hope you enjoyed this conversation. This show is a production of this week in Health It. For more great content, you can check out our website at www.thisweekinhealthit.comortheyoutubechannelatthisweekinhealthit.com/video.

Thanks for listening. That's all for now.