This Week Health

June 16: Today on TownHall, Jeffrey Sunshine, CMIO at University Hospitals talks with Bill about data quality, the pace and scope of change in healthcare IT, and using data for predictive modeling. How does he see the pace of IT changes transitioning in the near term? What is something he is looking to implement in the next year to add value to his organization.

Transcript

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Today on This Week Health.

We're in a motion phase. I try to set the expectation that no, I don't think it's going to settle and that part of the profession is managing that change. Now, does it always make people comfortable? Is it always easier? It should always get easier. But I think it's not going to settle until we get to the next stage on a number of things.

Welcome to This Week Health Community. This is TownHall a show hosted by leaders on the front lines with interviews of people making things happen in healthcare with technology. My name is Bill Russell, the creator of This Week Health, a set of channels designed to amplify great thinking to propel healthcare forward. We want to thank our show sponsors Olive, Rubrik, Trellix, Medigate and F5 in partnership with Sirius Healthcare for investing in our mission to develop the next generation of health leaders. Now onto our show.

All right. So here we are with Jeff sunshine, CMIO for university hospitals out of Cleveland. Jeff, welcome to show.

Thank you.

Looking forward to this. So we're at the Scottsdale Institute, a lot of great conversations going on. What, what do you take away from yesterday? What's what's top of mind for you at, at university hospital.

Those are two different questions

they are. So I'm giving you a choice, which direct

let's start with, what did we take away from yesterday? I thought yesterday was nice in at least for me introducing some of the perspective of the capital side of thought in generating new ideas the upside considerations in market which I don't always get a chance to think about.

Isn't helping run a large not-for-profit health system.

Right.

So, I, I. If there was a summary take home for me from that is that it's still an area, healthcare, it digitalization of healthcare of wide opportunity, innovation and activity.

I thought yesterday was interesting in that we had that breakout session where we looked at things like the staffing challenges with health systems.

We looked at incumbents, new incumbents coming in and value-based payments those sort of things. It's interesting to just have that time to, think about those things, cuz we are involved in the CMIO you're involved in the daily, optimize the EHR, make everything work, integrate new technologies.

Yeah. Sometimes it's good to do that. The other thing is just to hear the different perspectives. I mean, so as we sat there yesterday, were there, any new ideas that sort of jumped out at you?

All respect to the conversation. I don't think I left with, age, treasure of a, wow.

Let's go here that I hadn't thought of before.

Well, that was, they coalesced around similar, things.

Yeah. So, so let's take these in order and you asked me and your other question what's top of mind, the, the rubric of value based care. How do we con. Control our costs while we're delivering quality to simplify that formula, we've already had some real successes with that in our health system.

In 21, our Medicare advantage performance was extraordinary. We did, top five or so nationally in quality, and we're probably 15, $1,600 per year per member, less than anybody in Ohio. That's very so, so we have some success and that's probably built out of our work and our attention, but also. The mission we've always served, which is treat everybody with whatever they have, which is a really difficult mission born of the, academic world before we grew and became an integrated delivery network in a large region.

So, so top of mind for me is, it sort of buckets a few ways. One is the continuing. Transformation to computerized medicine, we've put in EMRs, but, and we are currently doing a shift in vendor. So that was a big project within us, but there's always corners where you discover, oh, there's still something else.

That's on paper that needs to get converted. And you thought you had it done, but you've gotta go back. And, and sometimes they're really meaningful and a project within a project might be electronic consent con consent documentation. And, and so those things are always in the bin of what's the shortest short term, what are we working on? And, you know, I'm a medical information officer, so I try to think about how can we be transparent with our information, use that to enable our providers and our patients. And I think most aspirational get into the predictive area, which for me is still the, the greatest opportunities. And.

and I find that even, even as we use tools, whether we call those algorithm machine learning AI or, or predictive math, that healthcare isn't so good at probability yet. And how do we teach that? Because fundamentally the third thing I do is large scale change management. Right. And you heard me ask a question yesterday.

About the implementation sciences and having merely a good product in healthcare is not sufficient

So what are you finding from an adoption standpoint? I mean, you have the, I assume an

EHR.

Yes. We have one and we're changing to another right

and you're changed to another. So you have that, that's a significant project. You have significant digital tools coming in and all sorts of new things. We have in the conversation around. Physician burnout and those kind of things. We're also introducing tools. It's, it's, we're always looking at introducing tools,

constant change,

constant change. Yes. How are the physicians feeling about that? I mean, to, to, to a certain extent I've, I've once had a physician say to me, can we go back to paper? I'm sure. We've all, we've all that. I've never heard that I say we've all heard that. Right. We've all heard that. And I don't hear that as, Hey, let's go back to paper.

Everybody understands the value of digital, but what they're saying is there's so much change. Are we ever gonna get to a, to a base where we don't have massive change and we can start to do incremental improvements on things?

Well, I think we all forgot middle school with basic mechanics, things in motion tend to stay in motion.

Things at rest tend to stay at rest and we are in a we're in a motion phase. So when I'm. asked That in a broad sense or an individual sense? I try to set the expectation that no, I don't think it's going to settle in that regard. And that part of the profession is managing that change. Now, does it always make people comfortable?

Is it always easier? It should always get easier. Is it always so no, we already know those, understand those answers. But I think it's not going to settle until we get. To the next stage on a number of things input you, we, we still demand too much of our doctors and nurses in just typing things in and whether it will become voice or we'll have more automation simply capturing information, or we crowdsource more, even to the patients themselves to get information in that's not optimized yet.

Ambient sound as an example we're experimenting On on how to do that organizing the information once it's in. So we, when we're viewing it, it's what we need to see. The primary care doctor needs a different view than the cardiologist who needs a different view than frankly, the bedside nurse or the ACO manager.

And can we really filter appropriately from what's become now a trove of electronic information. So now we've got input. We still have to. optimize We have viewing, we have to optimize along with the input, make more sense out of documentation. And the third and biggest challenge is can we take that data out and present it to people to help guide them to decisions faster, and better?

So those are the three areas that I don't think are ready to settle.

Are we, so we're the promise of data? Yes. The promise of digitization. That we're gonna have all this data and we're gonna be able to improve quality outcomes, reduce costs, those kind of things. But a lot of that is, is presupposes, good quality data.

Are we making progress around quality data so that we can overlay predictive models, AI machine learning on top of.

those things

The answer to that is simply is yes, we are making progress progress, right? I mean, you set the, you're not

gonna qual quantify

the, I don't think you can yet. I, think it's a continuous improvement cycle.

Yes. Right. And the more you start looking at what's what is the data telling me and realizing it's not what you thought it was gonna be in terms of quality or cleanliness, the more you go back and, prep your front end.

Yeah. That's interesting. So what's, what is. What's the thing you're looking at right now, or that you're implementing right now that you feel like has the most promise for, let's say value to the organization in the next year?

Well, I don't know. This is the most actually it's, it's a smaller example than that, but it's to follow our earlier question. I think we've learned that sometimes simple can be really powerful and I'll give you an example on the predictive. If, we can use the data that's in the system to produce a risk of mortality.

So what's happening in your data that makes us concerned that you have above normal risk, unfortunately, to, to end. And that might be the disease you have. It might be your vital signs. It might be the combination of, things that are happening because you have multiple chronic things that are now three or destabilized, there's all sorts of ways that might be.

And we can use that to help shape your care. We can use that because we can intervene sooner, get you care because you're still salvageable. We can intervene because we can talk to you about what this means for you and your family. And we can intervene because maybe it changes the therapy we might want to use.

It's it's very attainable already with the data that we have. And we're learning how to take something like that and use it. Constructively to shape care. And at the end of the day, I have to remind myself and everybody else, what are we doing this for? Right. We're doing this to make healthcare and patients experiences better.

Fantastic. Jeff wanted, thanks for your

time. Pleasure.

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