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  Welcome to This Week Health. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of This Week Health, where we are dedicated to transforming healthcare, one connection at a time. Today , we have an interview in action from the 2024 conferences, the spring conferences, VIVE in LA, HIMSS in Orlando.

Special thanks to our sponsors, Quantum Health, Gordian, Dr. First, CDW, Gozeo Health, Artisite, and Zscaler. You can check them out on our website, thisweekhealth. com. Now, onto our interview

hi, I'm Drex from This Week Health, and we're at HIMSS 2024, and there's a lot of stuff going on here. Yes, there is. So far, so good.

Yeah, it's been a wild week.

There's so many people here. Everybody's out and about. Lots of energy coming through this week. So, I think everybody's excited to see what's next in

healthcare. Yeah. And I'm with Kevin from ClearSense. And we're going to talk about A lot of different stuff. Yeah. Yep. I mean, let's start with you.

Tell me about your background, your history, how you wound up making your way to Clearsense.

Yeah. Yeah, absolutely. So, I've been in healthcare for over 15 years now. I got my start working directly at Epic where I was leading a lot of their implementations. So, really at the forefront for a lot of these hospital groups that we're going through the major initial digitization, meaningful use adoption, and go into these enterprise solutions for the first time.

While I was there, I really helped do a lot of the multi state implementation callouts for a lot of groups and then I spent the last about 30 months of my time there leading the implementation over in Denmark and bringing live two of the five regions over there. At that point, I really determined that I think my implementation days for EMRs was really I started getting really interested in what was going to be next.

And for the last about six years or so, I've been working at Clearsense, where we've been focusing on trying to figure out how we can take all of that data from all these different systems and make it something that's really organized and useful so that we can empower these organizations to make data something that is actually an asset they can take advantage of.

Data

driven decisions. Yes. First of all, You've got to be able to get to all of it, and it's not just VHR data. That's right. It comes from other places too. Absolutely. You have to put it into a place that , you can organize it.

Exactly, yep. And, tell me the rest of the story. Yeah, absolutely. Well, you're right.

So, even small healthcare organizations have hundreds of applications. Some of the larger ones have thousands, and several thousands of those applications, a lot of those are production level and they are using them actively. There's a whole host of those that are maybe not in active use, maybe they have a maintenance, but the actual data that sits within them is required to maintain or it's important for them for other initiatives.

So, what we really focus on is how do we take all that enterprise landscape of all those technologies and all those systems and derive value out of it. So, for some of those historical systems they might be left in the wake of major enterprise deployments of other solutions like their EMRs. So, legacy

systems that I really would love to turn off and stop paying

the license fees.

Exactly. That's another advantage, I guess. Exactly. You're still having access to it. That's right. So for legal medical records and retention and there are legal requirements, of course, around that. But the other part of that is that data is really valuable. These organizations have been gathering information for the last 5, 10, 15, 20, 30 years in these different systems.

They don't want to lose the value of all that information. Certainly that's important for retention purposes, but also important for things like research. When they start to look at building out new ways to look at analytics, that information is also valuable. So, we focus certainly on some of that legacy data and making sure that we have the most value out of it and retain the quality around it.

But we also then incorporate their active production data as well, so that we provide them a fuller landscape and a trusted data environment to access for other purposes

Is there an opportunity then to use that data and re inject it into existing systems to help them make decisions?

Yeah, absolutely. So, we really operate as a data management platform for them. So, connect to all those source systems as you said. Pull that data into our specific healthcare data domain structures. Which is allowing us to think about ways to organize that data that makes it really usable and understandable to the business users.

So, the governance part of it? Part of

this becomes really important, too, because sometimes it feels like you might, in a health system, have three or four or ten sources of truth for a particular item.

Absolutely. You're helping with that. Yeah, absolutely. Yeah, data governance and really making sure organizations are speaking the same language when it comes to data, it's hugely important.

We actually approach it in two ways. One is with the technology itself and making sure people understand how that can support it. But it's only as good as the people that understand it and that can really speak to it. So we also run a data governance advisory practice as well. And really what that is focused on is not us going in there and instilling what they need to be doing, and telling them how they need to operate, but building a culture around data, and data stewardship, and the way that we actually speak about data.

So, when you ask me about data, or have a data request, then I am responding in a way that is to your like and to your understanding. You create a common language. That's right. For a lot of these conversations. Absolutely. That don't, those don't exist today in a lot of places. Exactly. There's a lot of times where people are really speaking past each other when they talk about data.

So, we're really trying to make sure that people are speaking the same language. Absolutely. That people that are the domain experts are really empowered to be the domain experts. So if you know a provider credentialing better than anybody else, then I should lean on you to help me make those definitions.

, and the whole organization benefits from that. , from a user

perspective how do users access the system. Are there a limited number of users? How's it set up so that ultimately that usability comes back to the

Yeah. Folks who want to consume it? Absolutely. Yeah. It's a great question.

Well, it's ultimately it's really important to make sure that data gets the right person in the right technology or right workflow, right? , having another place for people to go log in or look for information that's. Not somewhere they're already going to be is challenging, right?

You can have a very powerful insight, but if nobody's looking at it, what does it matter? Really what we try to focus on is we do have some applications of our own that we can embed different places , things like looking at legal medical records For example, if they were to look at that for the last several years, we can embed that directly into their current EMR.

Right, so it's directly available with the network flow past the patient context through, past the provider credentials through. , it makes it really seamless to access. Does that

help in situations where maybe you have consolidated electronic health records from various partners? Absolutely. Down

to

one or down to two or something?

Like, you don't want to lose all

that other stuff. Okay. In the world we live in today, there's a lot of consolidation that happens. a lot of healthcare providers are either going to an enterprise solution, where they're getting rid of multiple EMRs. Maybe they're going through a lot of burgers and acquisitions, where they're also going through the same.

And we can aggregate all that information together. Pulled together and virtually merged so that there's one place to go look for all that information about an individual. If you have records in multiple systems, and now you're in your current EMR, you can select the link to go out to us.

It'll aggregate all that information in an easy to find place. Makes that the access easier, a lot less hunting and pecking. Trying to remember where, or what system it actually came from, just having it all in one spot. Yeah.

Data driven decisions. I mean, ultimately this comes down to, one of the things you hear people talk about all the time is that we're data rich.

There's tons of data, but it's not really been built or consolidated in a way that helps me do something better. That's right. Monthly reports, but I don't remember a month ago, why did the report come out like that? That's right. I need it more real time. I need it to help me change my behavior in the moment so that I can do better going forward.

And training me to do better too because it's there when I need it. So, tell me how you

guys help with that. Yeah, absolutely. That

consistency

really important because I think what we've all run into is people having distrust. in data or information that they're receiving. we're really focused on being very transparent around that full lineage of like, how do we actually get to that endpoint?

This is such an important part of this, right? It really is. I can tell you, as a long time healthcare CIO, it was not unusual for us to pull data together and generate reports and show it to physicians or others. And the immediate reaction was, there's no way that's right. Yeah, There's no way that's my data.

That's right. But being transparent, being able to show them the way. It's incredibly important in convincing them that this is your data. We're not here to make you , hang

you up about it. We just want to make it work. That's right. And that's where that, two prong approach helps with the data governance, building the culture of data so people can understand how to speak to it.

And then secondarily, with the technology having that transparency and that to it, allows us to say, oh, that's actually not, right. Actually, it is, like, based on what we had, and if we maybe were calculating that incorrectly, well, let's go fix it together, right? So we call that the clear box approach, as opposed to a black box

I like it.

I mean, I think that transparency, that ability for them to, like I said, tie back to governance. Understanding that, oh, well that's not the data source we should use to do the math to calculate that outcome.

right

That's right.

Well, at least we know where we got it. The other part of it is, it was never really unusual to go into an executive cabinet and have one group show up and show data that would indicate that we should do project A.

And then another group show up and, Apparently show almost exactly the same data, but , it looked completely different, which would lead us to project B. That reconciliation, too,

yes

Usually useful and helpful.

Yeah, absolutely. And that's really where having ClearSense sit in the middle, as really that trusted data environment, allows us to make sure that we have that consistency .

What we prevent that is having to go back to those source systems, do a data extract, do some level of curation or pulling that together, then deriving your insight and then delivering it. Even the same person going through that same process again, they're like, how did I do that last time? Or where did that really come from?

Every day is a new day. I didn't necessarily write

down exactly how I did that extract.

Exactly. Exactly.

So we really do it, It's almost like the old infomercials, right? You set it and you forget it, right? We configure that once, allow them to have that really lineage sitting there, that configuration, that output, and then it will be consistent all of the time.

It's documented for them, they can see how it works. Exactly. All the business rules, all the logic that was used, understanding the how we got there, really helps people with. the adoption

As systems change, and there are upgrades and new releases, in my head, that's gotta be painful. You've worked your way through

that.

Whitin our platform itself, we do a lot with data transparency and data quality. So we can see if information's coming in, if it should have a field that's populating, and now it's null, or it's something, that we'd expect it to be a letter or its a number, those types of things. We can do those types of quality checks along the way.

And ultimately, it allows us to go and look back upstream and say, how are we acquiring that data? And do we need to make a configuration change? Can we map the data.

That's right. Exactly. Exactly. So we can actually see that, but we're not finding out on the back end when it's actually being, used and leveraged for different groups.

. We're finding it in the middle.

Like the notifications

when something doesn't match, not when the

output comes. Exactly. Everybody says this can't be right. And you're like, yeah,

exactly. Focus heavily on quality. And recently, we actually got our certification through the NCQA around their DAB certification.

That is just further emphasizing the value. We're focused on making sure what's coming out of the sources are validated and accurate. The quality's there. We have all the processes in place and technologies in place to validate that and then feeding those things forward. , we use a lot of that best practice in our technology and in our approaches to make sure that people can trust the data because if you can't trust the data, then you can't trust anything else that you're doing with it.

I have friends at NCQA so to hear that you have that certification. I know that is not an easy

thing to do. It is not, yes. So, congratulations

on that. That was pretty amazing. How was that process?

It's pretty brutal, right? Yeah. I think it sounded simple, right?

I think on the surface, you go on check out the program and you're like, okay, that sounds great. We can do this. But it is, it's a lot of work. There's a lot from a technology perspective that it really requires you to show that you're confident within. It also makes sure that your processes are really our best.

So I think it's really helped us improve as an organization overall. We've also taken a lot of the work efforts that we've done and we've productized them. We actually built them into our solutions so that we made it easier to do things like primary source validation and looking for discrepancies.

and making sure that the data flow was populated in the right way. Even in going through the certification process, it's typically fairly manual if you do it standardly, but we actually productize that as well to make it even easier. Wow, that's

amazing. Have you seen anything on the floor, anything here that you found particularly interesting or somebody doing something that you said like, wow, I think we ought to figure out how to work with those guys?

Anything cool that you're

Yeah. I get, honestly, I get really excited walking around here and seeing the amount of innovation people are having with new technologies. I do appreciate, I know everybody's talking about AI and the ways that they're going to be applying these things to really help their organizations overall.

And I think it's really exciting. Extremely aspirational. The thing that actually gets me very excited about it is that, for us, those are really good partnerships for us, or are things that we can help empower. We all know that any of these AI component trees require good data. Good

data. That is the foundation of everything, right?

Maslow's Hierarchy of Needs. You can do cool AI, but

you can't do it. That's right. Unless you got the source. That's right. So, our focus and keeping laser focused on making sure the data is Quality, Trusted, Completed. I think it's actually going to help a whole lot of these organizations adopt these new technologies.

So, that's what really gets me excited is that I see more ways that organizations are going to be able to do better and use new technologies and there's a really good way for us to partner up and help out with that. So, it gets really exciting. Sounds

great. Kevin, thanks for the time. Yeah. really appreciate the opportunity to sit down and chat here in the ClearSense booth.

Yeah. Thanks. Yeah, really appreciate it. Thank you, Drex. Alright. Yeah, thanks so much. Of course.

  Thanks

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