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 Welcome to this Week in Health, it influence where we discuss the influence of technology on health with the people who are making it happen. My name is Bill Russell Healthcare, CIO, coach and creator of this Week in Health. It a set of podcast videos and collaboration events dedicated to developing the next generation of health leaders.

We have a few more channel sponsors who have chosen to invest in our show and in invest in our mission. And I'm gonna highlight, uh, one for each new episode over the next couple of weeks. Having, having dealt with a ton of companies around healthcare IT staffing, I found pro talent advisors approach to be refreshing.

Some of the things that I'd liked is that they are dedicated to healthcare. They understand the world that we live in. Uh, they're lean and nimble. They don't get bogged down where other firms do. They're entrepreneurial, they're creative, flexible, and, uh, willing to explore win-win scenarios. Um, they're a high integrity firm.

Everything is above board. They tell you what they can and can't do when you start. And I guess I would add to that list that they are, uh, dedicated to investing in the next generation of health leaders as evidenced by their sponsorship of this show. Uh, and I want to thank pro talent advisors and the rest of our channel sponsors who, uh, continue to make this content possible now onto the show.

Hope you enjoy. . Today I'm joined by Chris Harper, vice President of Health Information Technology at the University of Kansas Health System. Good morning, Chris, and welcome to the show. Good morning, bill. Uh, thanks for having me. Yeah. Uh, a second time. You are, I've done 152 episodes and, and, uh, the onsite ones are always a challenge and you are the, uh, the second one.

I just completely lost. I, I mean, unless people wanna listen to a podcast of, of, uh, a barista, uh, a piano in a lobby, and, uh, some people hanging out at the elevator. 'cause that that audio came through a lot, a lot louder than our audio. So thanks, thanks for doing this again. Well, I'm glad to know I'm not the only one that got lost, so.

Sounds good. . Yeah, I, you know, it's, it's, uh, it's just par par for the course. It's, it's, it's, uh, you know, I, I see now at some of these conferences you'll go and you'll see these people with big production crews. Our, our conferences have gotten pretty, uh, well followed and, and people doing different videos and stuff.

Of you who saw me at Chime and I saw you at Chime and, um, you, you know that I just used two iPhones and, uh, a couple of mics. And so, uh, since our show, I've upgraded all my equipment just so. So, so that'll never happen again. Hopefully you're going away from the high cost or low cost care. Is that, is that where you're going,

No, actually I got, I got a, an app on my phone, which, uh, drowns out the back background noise and, uh, it's, it's amazing what you can do on these phones these days. Of course, they're not. They're not cheap devices anymore, but it's, it is pretty amazing. Uh, all right. So let's, let's, let's get right to it. So tell us about, um, uh, university of Kansas Medical Center and, and your role specifically.

Yeah, so, um, so. I guess I joined, uh, KU Health System about seven years ago, and, and, and my initial ask, or, or my mission really was to, um, to apply some of the things I learned in different industry. Um, how do we apply data and analytics really drive, I. Better outcome for the, uh, patients. And really, we, we try to, to do the, the triple aim and now what we call the quadruple aim, which is, you know, um, improve quality, uh, improve access, uh, do it at a lower cost and.

Keep, keep your providers happy using your system. So those are kind of the, the things that we, we worked on. And so, um, so we, we started that journey about seven years ago. Um, but interestingly enough, my recent role, um, one thing that we recognized is. Today you can't really, um, split your data and analytics strategy from your application strategy.

And so as part of the, the combined role that I, I have on of, of serving as, um, we, we try to make sure not only the. The right technology and the right application, the workflows identified, but also, uh, the data that are going in are, are coming back out as a, a valuable insights that are our clinicians or, or our, uh, administrators or whoever needs that access to the information can act on it.

So now I'm responsible for both the, the, the backend data and analytics and the front end applications. So you started in the analytics role, uh, established the analytics prac practice and program, and then, uh, recently, what about six months ago or, or so moved into the application? Uh, last July, so more than six months now.

Okay. How's, how's that transition? Um, it's been good actually. So my, um, um. My former role, I, I used to manage, uh, software development and, and, and I used to lead, uh, a small agile team that developed a lot of custom development applications. And so we, you know, work for companies like, you know, Santa Fe, Venice, Pfizer, and consulting and developing, um, software to support their, their, um.

Sales efforts and, and, and, and different type of engagement efforts. And so I feel comfortable, um, with the, the additional responsibilities. Um, it, it, it does make sense now, um, and, and how a lot of different systems need to come together to kind of drive that mission of that, that quadruple name. And so, um, I was glad to, um, to take on and.

Uh, luckily we had an amazing team already established, so it was really an easy lift for me. But, um, you know, really right now what we're trying to figure out is how do you not only use the data more effectively, but you know, how do you gain efficiency out of it? And so we are applying a lot of different, um, you know, strategies, whether it's automation or, uh, different alignment of how our teams are, are.

Aligned. So we're, we're doing more of the Agile methodology within our Epic environment as well. So we're applying a lot of different, um, um, analytics and software development methodology to improve how we're delivering our services. Yeah, so when we first met, uh, colleague, uh, put us in touch, I was doing some work for a client and it was around Health Catalyst and.

You guys, uh, brought Health Catalyst in as part of, um, part of your program early on. Um, so it, it actually, what we're gonna end up doing is we're gonna, we're gonna end up talking really strategy, architecture, operations and innovation around the data and analytics program for, and, and that's where we're going to sort of camp out.

'cause what I've found as I go out into the industry is that there's a lot of health systems are still struggling with, with some of the things that, uh, you and I have talked about. You and I talked about on that initial call. So, um, actually walk us through the process of standing up the data analytics program.

And so, um, I did about three, three months engagement, you know, um, speaking with, um, a lot of the physicians and physician leaders and our executive throughout their health system and trying to get a gauge of. How mature are we in, in using, um, data as a strategic asset to really drive, um, whether it's your, you know, profitability, profit margins, better outcome, you know, better research, uh, what have you.

And so, um, I, I've did about data collection and analysis, um, of our current. Capability and maturity. And so, um, typically I like to, um, try to have a framework around that that's shown. Not only, um, you know, opportunities to improve, but also you know where you are at as you go because every organization starts at a different place.

And so, um, after I did about three months of, of, of exercise, but collecting, understanding where we need to go, um, I initially asked to form a, a data governance group to be able to start helping. Me and the organization make the right decisions around our, our, uh, data journey. And so I remember, uh, um, you know, my, my CIO at the time, uh, told me that great idea, but, you know, um, probably something that we don't want to do.

Um, you know, to be honest, you know, later on he told me he thought it was the dumbest idea. But, um, you know, he came around and, and, and, and basically, you know, um, what what ended up really getting us going is, you know, as I'm doing my assessment understanding, um, I, I said, you know what? Um, there's gonna be becoming a time that if we don't do this and, and have a, a really, uh, a group that governs our information and data, you know, we're gonna come into a, a, a place where we're not gonna have a synced up, uh, um, information or metrics.

And so, um, what I use at the time is I used to kind of tell like, Hey, you know, watch out for this, this thing that could be coming. And really, um, you know, I think it's. Churchill who, who said, don't let a, a good crisis go to waste. Right? And so what happened was, um, you know, we, our, our organization was on the old SMS, uh, revenue cycle system for 28 years.

And, and that's, um, that fall we were going live with our revenue cycle, um, with Epic. And so I, I kind of made a prediction that if we don't really align our metrics, the data, the sources, systems, and all of the things that typically come with your data governance and ED. We're gonna come to a place where our data is really not gonna make a.

And so typically that happens after about three months after you, you, you switch over systems. And, and so my prediction came true when, um, there was a board meeting. Um, uh, after three months we went live with our revenue cycle. And because we have different reporting groups, uh, reporting on different things, but using the same metrics are, are.

CFO and CO kind of presented, um, same metrics but with different, a little bit of different numbers. And so that's where, you know, uh, um, I got a call that night after the board meeting saying, Hey, you know, that thing that you talked about, maybe doing data governance, um, maybe we need to get that going.

And so I got the, the thumbs up on, um, getting that going. And really, um, that's the genesis of our, our strategy because as a technologist, you know, I'm probably not the best person to really, um. Set out the strategy, but also, um, really drive towards this. What I need is a, a coalition of the willing, so to speak, to be able to bring the group together and making decisions together.

And so, um, right after we got our data governance going, understood where our current state is, and then we laid out our. Opportunities to improve. So I used a, a three-year roadmap to be able to highlight, um, whether, you know, it was a technology need, whether it was the, the capability needs from, uh, data literacy programs or different things that we need to have in our organization.

We used that roadmap and got the blessing from the data governance group to be able to lay out that strategy. One of the key things that, you know, um, that, that we needed was, um, as part of my assessment, one of the things that I looked at is, you know, at that time we had about, um, eight plus different reporting groups, um, 12 different source systems that we were reporting off of.

Um, but the big chunk was we had 60,000 access databases that acted as a data warehouse for the organization. And so we, it was not a sustainable model. And so one of the things I, I, I partnered with one of the, the reporting groups is to be able to, um, measure not only their demand of all the data and reporting requests, but also the throughput and where they're spending most of their time.

And quickly, what we realized is. There were, um, a ton of, uh, opportunities around how we not only, uh, collect the data, but how do we report off of it. So, uh, if you, if you have a typical reporting individual, as requests are coming in, they're spending, uh, around 30% of their time hunting and gathering data.

Trying to normalize it, put it into this access database for it. 'cause we didn't have a, a, a system wide, um, whether it's a SQL environment or different things to be able to provide that, that data, um, normalization. So they used access databases. Well, you know, so if you look at, um, the demand at the time, uh, roughly it was about 11 to 12% year over year.

That demand was. Only way to really meet that challenge for the health system as we're growing was just hire more people. And I quickly showed using, um, the data to say, Hey, you know, that's probably not the best strategy. 'cause year one we have to hire two people. Soon after that we're gonna have to hire more and more and more, but we're gonna be, you know, being the same process and getting the same value out of it that, that, that we're having issues with.

And so, um, as part of the data governance journey, uh, we did a commission study to look at. What type of a data warehouse capability that we wanted to go after. And so, um, you know, everybody knows at that time Epic had, um, their own capability around, um, their, their data warehouse solution. And so we, we compare what was there and where we're at and, and what the marketplace had.

And one of the things that, that I've always done as a analytics leader is. You know, um, I don't wanna spend a dime of our organizational money on something that adds no value. So one of the key things that we focused on was the time to value. So if I, you know, start spending the money and, and standing up this EDW, we wanted to make sure we could have actionable outcomes and improvements showing within the, the first six to 12 months.

And so that was one of things we looked for. And when we looked at, at that time, the marketplace, not only the. The vendor who knew how to do improvement, whether it's care or cost, um, we both need, and not only the, the expertise, but also the, the technical capabilities. And so when I looked at the marketplace, um, health Catalyst as the vendor that we ended up going with had not only the.

The, the people and experience and the knowledge, but also, um, had the technical alignment that we were looking for. And so it was a, um, after a vendor selection, uh, we went with, um, health Catalyst on their care variation improvement program and also standing up their EW platform. And, and I would say the key thing that aligned with, um, their organization, not just the technology, but.

You did your own process for evaluating the, the system. Um, I know that HIMSS has their framework now. If, if somebody were starting us off today, is that the direction you would point them? The, the, the HIMSS framework for evaluation? I think the HIMSS framework is good. Um, I believe at that time I used the advisory board had a pretty good framework around the maturity model, and the reason that that resonated with me is really, um, you know, at ku our organization was already.

Very savvy about using data to make good decisions. So, um, we had a very, uh, uh, datadriven culture that that was an easy, um, opportunity for us. And so what, what advisory board, uh, framework data is really kind, had difference. Um. Capabilities, whether it's the data literacy, whether it's the um, um, the, the, the technology alignment from a BI perspective, what have you.

So that really resonated with where we were at. And so, um, that's how we adopted it. I think the HIMSS model is, is, is, is very broad, maybe a little broader than, than what, what. We needed it at the time. And so we, we used the advisory board and we modified it for our, our own internal usage. So it's not exactly as, um, how advisory board drew it up, but kind of model.

Went down to probably the next level layer of details, um, uh, more than what hims um, analytics, uh, maturity provided. We'll get back to our show in just a minute. Three, industry leaders came together and decided to create a firm that brings wisdom to healthcare organizations when you need it. Most STARBRIDGE advisors has aggregated and vetted industry veterans in the areas of digital strategy, roadmap development, data, and information governance.

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I personally want to thank Starbridge for becoming a channel sponsor of this week in Health IT and investing in developing the next generation of health IT leaders. Now back to our show. You, you seem to be communicating that each organization's culture and data needs and, uh, maybe even systems and governance is a little different.

So you almost have to spend that, that, that time digging into all those elements to make sure that you're identifying the right program. It's not a one size fits all kind of thing for healthcare. Is that, am I hearing you correct? Yep. That's, that's, that's correct. And then you also have to, um, look back and, and evaluate that every year.

Right. So we essentially developed this three year roadmap to kind of guide us where we need to go, whether it's with funding, with, um, additional. Opportunities. You know, when, when, when I started, um, that journey, uh, quality and safety obviously is our number one focus. And so, but at that, at the same time, we had opportunities to look at our, uh, improvement as effort with cost opportunities.

And so, you know, what I typically do is since you know, it's easy to count with your one hand, I look at five different. Opportunities or questions you're trying to address using data and really, uh, spending most of your time understanding not only where you're starting from a culture, technology, the people and all of that, but also are the five most important things that your senior executives or your physicians are looking for.

And really focusing on that, um, to really understand where your starting point is. And then really. If you wait, you know, a year or two to get value out of it, you've already lost the battle, right? So you've already got funding for maybe supporting you for 12 months, but if you're not showing your incremental value and.

You're using this, this really resource to, to, to improve outcome. You know, you're not gonna really get that next year of funding. So, so really we looked at what are some of the five questions and then, um, quick wins we can have. And so, um, uh, to use an example, um, at that time are heart failure readmission rate was, was pretty bad.

So we were around like 22. Eight, uh, right around there. But we had a, a, a young physician that we just hired to help drive that program. And then they've already had a established, um, kind a care improvement group that was starting to dive in. And so that was an easy, uh, marriage for me to work with that group and trying to help them improve their 30 day heart failure readmission and really bring the, the, the missing pieces that, that they needed, which was the technology and the data.

And so. We quickly partnered with our physician champion and nurse champion since they already are focused on improving from a change management perspective and, and care delivery. You know, within the first, you know, six months, we got the environment stood up, really hyper-focused on it, uh, developed what we call our analytics applications.

So they have the, the data at the fingertips that, that they defined they needed. And once we got that going, um, it was an easy, again, easy lift for, for us. But we had a, a. A clinical partner that was really willing to help. And so after about 12 months of actually, um, um, having the analytics application and the tool and the care team to really use that to improve, we went from 22.8% down to about 18.5%.

And so that was a tremendous improvement within the first 12 months. And then when we back in measure, you know, what was the, the cost opportunity, not only, you know, better care for our patients, but also. What was the opportunity from a cost savings? Uh, really it was about half a million, or sorry, $800,000, um, uh, cost reduction opportunity that we saw.

Um, and so that was an easy thing for us to, to really hang our hat on. So really, you know, within that program and couple of others, we already, um, paid for the investment we've made in our EDW structure. Yep. So, um, a lot of our users like to ask me questions around or want me to ask questions around budgeting.

So you, you sort of described the initial sort of foray into this was, you know, at a board meeting they're looking at different numbers, which is more common than I think people care to, uh, care to acknowledge. Uh, the data definitions within organizations is, is, is a challenge. So that's sort impetus for get moving.

Throw $20 million at you and say, make it happen. Or did they say, here's, here's a, here's a little tranche of money. Figure out what you can do around analytics. And then you had to justify it at every, at every stage kind of thing. So it, it was more the latter, but little, little, um, um, less painful. So I, I, you know, the way we build out our budgeting is, you know, what will it.

What will it take to get us going? And luckily we had a vendor who was willing to partner with us, um, in, in, you know, kind of structuring the contract in a way that allowed us to really kind of quote unquote pilot and, and, and, and test drive the capabilities. And so, um, so we initially structured it in a way that.

Within six months and 12 months of getting the, the project kicked off, um, how do we budget for those pieces? And then, uh, once I was able to show how, um, whether it's, you know, revenue generation or cost reduction or, or quality improvement, um, I always partner with our CFO and, and the financial team to, uh, have a, a way to measure that out from a cost perspective and then showed.

You know, the, the, the, the value that we were adding once we met the live. And so, and we make sure that the, uh, the data governance group through that, that, um, venue that people are aware of the value we're adding. And then, so to ask for that next year's funding after you got that going, and once you can show what the, the value that you're bringing, it was an easy sell.

So then we, you know, uh, rather they go one year after year. Then we built out a, a kind of a multi-year budgeting to be able to kind of grow the capability. And then the other thing I did is, um, um, we, we, not only from maturity but also tier, stepped our, our investment strategy as well. So typically, um, really

Where you wanna invest a year one is getting the foundational, uh, pieces, uh, uh, in place. So we call that the, the operational reporting, uh, investment. So that's where, you know, if you're not on the EDW, but your, um, you know, data visualization tool and the data management and kind of the business intelligence teams to be able to, to use that information.

To develop these, uh, um, whether there's dashboards or reports or whatever. And so we really hyper-focused on that investment, um, to get the operational and foundational technology pieces paid off that first year, year, and year and a half. And then we started getting into some of the other fancier, you know, more, more sexier side of the data with, you know, predictive and advanced and analytics is.

The other thing with that that we always hear is, you know, when we get our analytics strategy going. They're like, okay, when can we do some of these other stuff? Like, you know, people always talk about, you know, big data or, or AI or machine learning or naturally. So they, they talk about those things, but they'll understand that it takes upfront investment from getting the foundation built before you can really, uh, make an impact in those advanced analytics techniques.

And so. So we had a, a, um, it's almost a five year program where, so we're kind of at the tail end of that now, where, um, so we, we invested into the operational, uh, reporting foundation so that, that really, um, solidified, uh, I would say about 80% of our data and reporting needs. And so we can service our organization from that capability.

And then we started to build in our predictive and advanced analytics layers on top of that. And so, um, it took a little. I would say a pivot, um, um, as part of my role as well too, where I partner with, um, uh, we have a group internally called, um, lean Promotion Office, led by, uh, one of our physicians where that group is using the Lean Six Sigma methodology, um, sorry, uh, lean, um, Toyota Management Systems to be able to do process improvements, incremental improvements.

And so, um, once we got to a stage where we were ready to kind of really do the. The advanced analytics capability, I partner with that group. Since again, I'm, I'm more more focused on the technology strategy and the data manager strategy and, and the IT component of it. Um, I always look for a, a, uh, champion who can really partner.

I. In this journey. And so, you know, back five years ago, partnered with some of the physician champions driving the care variation Today I, I have a, a, uh, lean, uh, improvement group that I partner with to be able to really, you know, um, use the information technology that, that, that we're putting together and for them to use that to drive the most important things that are happening to the organization.

So you guys have really, I mean, you've come a long way, but the, you know, all these programs are touch and go in the beginning and you advocate quick wins. Um, which, which wins did you target, and which ones panned out and which ones maybe, you know, either took too long to, to pan out or so, which were good, quick wins to shoot for, and which ones didn't really pan out for you?

So again, every organization is a little different in their need, but we are, we are hyper-focused on, um, patient satisfaction and quality. And so the five initial, um, things that we tried to improve on as a quick win was, uh, patient, um, satisfaction, um, dashboards, kind of what ended up being, but so, so we, we are, are hyper focused on understanding what our patient needs are through that.

Uh. Opportunity. And so, but it was such a cumbersome effort to collect that data and send it out every, um, I believe it's every month, uh, or, or now I think we've gotten better than every month, but we send out this, you know, 400 page PDF. Every provider, then every, you know, nurse managers and so forth. And then they have to kind of really sum through that, that PDF to understand where their patient satisfaction was.

And it was the static data so they could look at it and get additional granular information, but they have to actually go somewhere else to be able to get additional information if they have additional questions. And so, um, we really focused on that. We. Manually test it to, to produce that report, but also on the consumption side of really adding value.

Um, I think people using it, but. Using it differently. And really it wasn't able to answer a lot of the questions that, that they probably have. So for instance, if their patient's satisfaction is going down in certain, you know, things like, um, nurse communication, like they have a lot more questions about, okay, what do I need to do about that?

Well, so what we focused on as one of the quick wins was, you know, um, let's get that data into our EDW environment, develop a self service tool that could be more comprehensive and set it up in a way that, um. As your typical nurse manager might have questions about their unit on their patient satisfaction, we, we served up different components of the data to be able to answer those questions for them.

And so, so that added a, a quick, quick, quick win and quick value. So, um, you know, getting the data in, developing these analytics tools to be able to help me answer those questions. And then mentioned about the heart failure one again, we're focused on the improvement. There were things that we looked there, um, epsis was the other one that we were able to, um, uh, get it up, uh, rather quickly and start really iterating.

And that's, that's the other component is, uh, whatever you develop, um, it's not gonna be perfect the first time. So you have to apply that agile methodology. And so, um, you know, so we do a, a, just like a software development effort, we do releases, um, every two to four weeks of that. You know, if, if you know this, uh, uh, patient satisfaction, um, application, analytics application was stood up, it doesn't have everything that everybody needs right away.

So then, uh, we commit to, uh, uh, having optimization enhancements every two to four weeks so that the users, when they ask for something that, you know, it might, they might not have it today, but they might get it in, you know, the next release or maybe release for that.

Sorry, I keep muting because the, uh, guy across the street decided to trim his trees right now with the chainsaw . So just, just seems to be, no one wants us to record. So, um, how, how did you measure it at first and has that changed in terms of how you measure the effectiveness of your program over time? Um, so I think we've, uh, so we always try to add value, so, um.

We always try to measure in terms of, of opportunities and savings. So we, we always measure, you know, we invested this much into it, are we getting the value out of it? So, so we measure from a, a kind of an ROI type of a, um, uh, assumption. Again, you know, if you talk to any organization who does an ROI model for their data analytics.

A hundred percent meaning that, you know, CFO's will always ask, well, did it actually take dollar out of my bottom line by investing this? Well, it's never exact science, right? So meaning, you know, it's just a technology investment in people investment, but ultimately it's how you use the information to drive, uh, change in progress.

And so, and that's where today, um, you know, one of the things that, that I, I really appreciate partnership with are. Our lean team is that they're, they do kind of a three to one investment return. So, um, they have to show three times the savings or improvements to be able to ask for another, you know, a dollar or an FTE or so forth.

So they really help us to measure some of those, uh, progress in the value that we're. Bringing and, and, and, and you know, the hard thing is getting over that initial, uh, big capital investment hump. 'cause once you get over that, uh, your, your ROI gets a lot easier. Um, but yeah, that, that for a lot organization, that's where they get really, uh, hung up on is, man, I gotta invest this, you know, 6, 7, 8 figure investment up front to get the value out of it.

You know, whether it's. Three year return, five year return. Um, but, you know, really you gotta look at it as a long-term, um, strategy versus, you know, every year return kind of a thing. Yeah. All right. Well, in the last five minutes, I want, I want to, we hit strategy, we hit operations. Uh, I wanna talk, uh, architecture and, oh, sorry.

Architecture and innovation. Um, so from an architecture standpoint, you choose, you chose to stand up Health catalyst. And you know, that's a point in time decision. There's probably factors involved there, but you're also utilizing, uh, other platforms as well, uh, like, like Epic for operational reporting as well.

Uh, walk us through, you know, how you think about, uh, platforms within the data analytics, uh, in, in terms of the strategy and the architecture. How do you, how do you think about different systems and, and how they fit into the overall, overall framework? No, that's, that's a great question. And, um, you know, my background actually, um, is, is before I came to KU, was really driving kind of the, the, um, architecture and what we called a, a, a capability modeling components to be able to, to really put together a strategy, whether it's your analytic strategy or whether it's your.

You know, um, um, you know, digital strategy or epic strategy. And so, but my background is then be able to kinda, um, develop this, uh, um, framing of, of people, process, technology to really, um, you know, identify the need in the strategy. And so, um, from a, a, our overall data analytics from a, um, architecture perspective is, you know, the simple question I asked is, you know, do we want.

As an organization, um, you know, have ultimate control of what our data, if we truly believe data is the, you know, our strategic assets. Um, I think there's some, um, um, um, economist magazine identified, you know, like I think last year or two years ago that today data is more, um, valuable than oil. And so, you know, so, so simple question.

I asked for our data governance and our senior executive is. Do we truly believe in, in that data? Ultimately is is our our most strategic asset. Do we, how, how do we want to invest into that? Meaning do we want to hang our hat on? Then, you know, at that time, you know, we had an opportunity to invest into whether it's Epic version or the others, and the answer was, um, nos, meaning that we wanted to have ultimate control over our data destiny.

And so our architecture really lays out, you know, when we started, uh, health Catalyst had the rights, um, from a a capability perspective to really get us going where we need it. But quickly we realized, uh, there are, are things that Health Catalyst is not good at, um, or doesn't even do. So for instance, we had an internal organizational need around, um, uh, doing a better job of recruitment.

So we were building a new, um, uh, new hospital power, and one of the things we needed right away is there's no way that we are gonna be able to hire people fast enough to, to support this new tower that's going up. And so through the data governance, one of the requests that came in is, Hey. Can we use a data and our analytic capability to, to improve our, our, our speed of recruitment and, and the quality of recruitment.

And so I partner with our HR team to be able to stand that up, but Health Catalyst doesn't offer anything around that. And so when we initially, um, uh, structure our, our relationship with Health Catalyst. Um, we, we had the ability, so, uh, uh, from a platform perspective, we built it on our Microsoft sql 'cause we knew that, um, internally we had, uh, a tremendous support from our infrastructure team who are, are, are well skill skilled in Microsoft.

Products, but also, uh, we had a lot of, um, uh, skills that are wrapped around that. And so from a technology platform perspective, we, we, we partner with Microsoft quite heavily and then started to invest into that multi-year, year, year strategy. Um, and so Health Catalyst is really, um, what I considered a sister EDW environment.

We have our internal one that's on the same platform, um, but has a different mission that it serves. And then, uh, same thing on the research side. So right now we're talking about. How do we do additional research, uh, using the same platform approach? Um, so we have a consistency around our technology infrastructure, but we, we can spin up another, uh, um, kind of a, a sister EDW that's really focused on different types of research.

And on top of that is where now you have opportunities. So I use what's called a, a service bus model. Um, something I learned, um, as a software guy, um, while back where, you know, all of that's great, but you have to have a, a cohesive data management. Uh, uh, strategy in order to be able to align all the, all the data and, and, and, and that's coming in and out of your, your, your analytics platform.

So, so I've been applying a lot of those architectural methodology to really, um, at the end of the day, it's, it's not just this one, you know, thing that he cows provide, but also. Uh, the, the additional needs organization have, and that also marries and obviously the, the Epic capabilities as well. So today we, um, haven't had the need to invest into the caboodle strategy yet, but I always tell our organization that it's not.

But when we need to invest into that, because there will be a need or a business case or a use case that Will, will say, Hey, hey, here's what I need and, and here's the value that'll generate out of our, our epic caboodle strategy. So today we haven't had the need 'cause we've been able to service all the, the needs through our other, uh, existing platform.

But, but I think quickly we're gonna show, uh, that we're gonna have to invest into, um, the, the epic caboodle strategy as well coming, coming soon. Yeah. So, uh, in the last couple minutes here, let's, let's sit on innovation real quick. Where's innovation gonna come? I mean, there's obviously incremental innovation that you're looking at, but where, where will innovation come in the area of data and analytics or where, what are you keeping an eye on right now?

Because back then. You know, spinning up a server or or data environment is hugely expensive. So most of your, your dollar went into trying to have a really good strategy with your infrastructure, whether, you know, so back then we partnered with organizations like Teradata, um, you know, uh, Informatica to really help us to kind, uh, uh, build that capability out.

Today, you know, a lot of it is really commoditized. So whether it's, you know, AWS from Amazon or, or Microsoft Azure, you know, there's a way to get around the infrastructure, um, capability, challenging. 'cause the innovation. I've came so far in the, the. Server storage and all of that. Even how you visualize data, whether, you know, one thing that ClickView was really, um, was good at is, is they have an in-memory capability to be able to quickly, um, go through the logics that you develop, uh, when you develop a dashboard.

And back then, like nobody else had that kind of capability. So they were really, uh, um, taken to market by the corner and, and really driving the data ululation, uh, capability. What I'm focusing on now is, is all of those things have became pretty much, you know, um, standard functionality or, or commoditized from, from, um, storage and server and computing power.

What I'm really keen on is there's so much information and data, um, that really how do you apply your. Machine learning and AI is where I'm really hyper-focused on. So you hear all of these partnerships that are happening now, whether it's, you know, between Google and Cleveland Clinic, or, you know, there's a lot of organizations really trying to take benefit of these AI and machine learning capabilities.

You know, I think the, the, the, the, um, the winner hasn't been really, uh, been clearly defined. And, and so what I'm doing is just watching. What opportunities they there? There are, you know, we are also applying some of the automation into our strategy. So, for instance, you know, once I was responsible for our EMR and Epic that I knew, um, I had to apply some automated way versus just keep hiring, uh, more people to do, uh.

Some of the manual work. And so the key area that came up was our testing. So, so we, we actually integrated and implemented automated testing with our Epic capabilities, and that's shown, um, tremendous, um, ROI within the first five months. That's kind of where I think I'm really focused on is from a data analytics perspective.

What are some of the machine learning and artificial intelligence that can really. Help you automate, uh, but also, um, help, help you focus. 'cause right now, um, there's so much data, but, but really a lot of it isn't really actionable. So if there's a way we can actually innovate using some of those capabilities within, uh, ML and ai, that's where we can really, um, uh, make advancement in innovation in healthcare.

Fantastic. Well, we're close to the end of our time. I love your, is that a, uh, uh, floor to ceiling whiteboard in the back? Oh it is. Yeah. So, um, you know, hopefully, I don't know if you can't read that, but that's kind of our, our our, so we, we try to convert all of our, um, office space into multi-use function.

So I open it up in a way that, you know, 'cause the, the, the, like most health systems or hospitals. Square footage is, is valuable resource as well. Yep. And so, uh, if you have, um, so we converted all the offices into having a, a whiteboard, a wall or a whiteboard space. And then if my teams need, you know, um, have a meeting with less than four people, they can use my office as part of that as well.

Yeah, I, I made this mistake. I actually did that and, uh, I couldn't reach above like six foot, 'cause I'm not that tall, but I had a guy on my team who was six foot eight. So he always took the, the top sections. Nice. No, that's, that's smart. . Great. So Chris, thanks for for coming, uh, coming back on the show. I really appreciate it.

Um, is, is there any way people can follow you, uh, social media, LinkedIn? I. Um, you know what, I, I don't do enough of social media. I, I am more of maybe kind of an old school as a technology guy's, kinda interesting. But, um, you know, if anybody wants to contact or, or reach out, um, you know, happy to work with them.

Um, but yeah, so I mean, I think on, on my LinkedIn, uh, I do have a public profile and if they wanna shoot me a message, I don't. I guess post anything because I, I just, I'm just kind of focused on, uh, the work I'm doing here, but, um, yeah. Happy to connect with people. Well, I love it and I appreciate you took, took my call a while back and, uh, helped me out with a client project that I was doing and, uh, really helpful.

I mean, just the amount of experience you've gained over those, uh, six or so years of implementing that, that program there has, uh. It is really, I think, valuable to the industry and and I appreciate you coming on the show to share it. Yeah, no, happy to do it and thanks for having me. Me, bill. Special thanks for our channel sponsors VMware, Galen Healthcare, Starbridge advisors, and pro talent advisors for choosing to invest in developing the next generation of health leaders.

Please come back every Friday for more great interviews with influencers, and don't forget, every Tuesday we take a look at the news, which is impacting health. It. This show is a production of this week in Health It. For more great content, you check out our website this week, health.com or the YouTube channel.

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