February 7, 2020: Chris Harper stood up an extremely effective data, analytics and governance structure for the University of Kansas Health System. These days you can't really split your data and analytic strategy from your application strategy. Join us today for the discussion.
Chris Harper of University of Kansas Health on Data and Analytics
Episode 181: Transcript - February 7, 2020
This transcription is provided by artificial intelligence. We believe in technology but understand that even the smartest robots can sometimes get speech recognition wrong.
[00:00:00] Bill Russell: 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 podcasts, videos, and collaboration events dedicated to developing the next generation of health leaders.
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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.
Chris Harper: Good morning Bill. Thanks for having me.
[00:01:30] Bill Russell: Yeah. A second time. You are, I've done 152 episodes and the onsite ones are always a challenge and you are the, the second one.
That I just completely lost. I mean, unless people want to listen to a podcast of a barista, a piano in a lobby and, some people hanging out at the elevator cause that audio came through a lot, a lot louder than our audio. So thanks. Thanks for doing this again.
Chris Harper: Well, I'm [00:02:00] glad to know. I'm not the only one that got lost.
Bill Russell: I, you know, it's, it's a, it's just par for the course it's, it's, it's, 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 conference has gotten pretty, well followed and people doing different videos and stuff. And, Those of you who saw me at chime and I saw you at chime and, you know, that I just used to iPhones and a couple of mikes.
And so, since our [00:02:30] show I've upgraded all my equipment, just so. So that'll never happen again. Hopefully
Chris Harper: You're going away from the high cost or low cost care. Is that?
Bill Russell: No, actually I 'vegot an app on my phone, which, drowns out the back background noise. And it's amazing what you can do on these phones these days.
Of course, they're not. They're notAcheap devices anymore, but it's, it is pretty amazing. alright, so let's, let's, let's get right to it. So tell us about, University of Kansas medical [00:03:00] center and, and your role specifically.
Chris Harper: I guess I joined a K U health system about seven years ago. And my initial ask or, or my mission really was to, to apply some of the things I learned in different industry. how do we apply data and analytics really drive a better outcome for the patients. And really we try to, to. Do the, the triple aim.
And now what we called the quadruple aim, which is, you [00:03:30] know, improve quality, improve access, do it at a lower cost and, and keep, keep your providers happy using your system. So those are kind of the things that we, we worked on. And so, so we, we started that journey about seven years ago. but interestingly enough, my recent role, one thing that we recognized is.
Today, you can't really, split your data and analytic strategy from your application strategy. And so as part of the, the combined role that I have on are serving as, we, we try to make [00:04:00] sure not only the. The right technology and the right application, the workflow is identified, but also the data that are going in are coming back out as a valuable insights that our, our clinicians or our 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.
Bill Russell: So you started in the analytics role, established the intellect prior practice and program. [00:04:30] And then, recently what about six months ago or so
Chris Harper: more than six months now.
Bill Russell: Okay. How's how's that transition?
Chris Harper: It's been good, actually. So my, My former role, I used to manage software development and, and, and I used to lead a small agile team that developed a lot of custom development applications. And so we, you know, worked for companies like, you know, Santa Fe, Inez Pfizer and consulting and [00:05:00] developing, software to support their, their, sales efforts and, and different types of engagement efforts.
And so I felt comfortable, With the additional responsibilities. it does make sense now, and how a lot of different systems need to come together to kind of drive that mission of the quadruple name. And so, I was glad to, to take on and, and. luckily we had an amazing team already established, so it was really an easy lift for me.
But, you know, really right now, while we're trying to figure out is [00:05:30] how do you not only use the data more effectively, but you know, how do you gain efficiency out of it? And so we're applying a lot of different, you know, strategies, whether it's automation or a different alignment of how our teams are, are.
Or aligned to where we're doing more of the agile methodology within our Epic environment as well. So we're applying a lot of different, analytics and software development methodology to improve how we're delivering our services.
Bill Russell: Yeah. So when we first met a [00:06:00] colleague, put us in touch, I was doing some work for a client and it was around health catalyst. And. You guys brought health catalyst in as part of a part of your program early on. so actually what we're going to end up doing is we're going to, we're going to end up talking really strategy, architecture, operations, and innovation around the data and analytics program for, and that's where we're going to sort of camp out.
Cause what I've found is I go out into the industry is that there's a lot of health systems are still struggling with, [00:06:30] with some of the things that you and I have talked about. And you and I talked about on that initial call. So actually walk us through the process of standing up the data analytics program.
And so a walk through memory lane go all the way back, you know, six plus years, and walk us through the process of standing that up at, at the, at the medical center.
Chris Harper: No, I'm happy to do so. And so if I go back, seven years ago, it was October, 2000. [00:07:00] 12. And, and really, when I first came in, you know, the first thing that I always do as a, as a, as a leader, trying to establish a new strategy is really understand, you know, where we're at and what do we need.
And so, I did about three, three months engagement, you know, speaking with, a lot of the physicians and physician leaders and our executive throughout their health system and trying to get a gauge of, of. How mature are we in, in using, data as a strategic asset to really drive, whether it's your [00:07:30] profitability, profit margins that are outcome, you know, better research, what have you.
And so, I, I did it by data collection and analysis of our current. capability and maturity. And so, typically I like to, try to have a framework around that that's shown that only, you know, opportunities to improve, but also, you know, where are you at as you go because every organization starts at a different place.
And so, after I did about three months of, of, of exercise of collecting, understand [00:08:00] where we need to go, I initially asked to form a. He did a governance group to be able to start helping me and the organization make the right decisions around our, our data journey. And so I remember a, you know, my, my CIO at the time, told me that great idea, but, you know, probably something that we don't want to do.
you know, to be honest, you know, later on he told me, he thought it was the dumbest idea, but, And he came around and, and, and basically, you know, what, what ended up really getting us going is, you know, as [00:08:30] I'm doing my assessment understanding, I said, you know what? You know, there's going to be becoming a time that if we don't do this and have a really, a group that governs our information and data, you know, we're going to come into a place where we're not going to have a sinked up, information or metrics.
And so, 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, you know, I think it's. Churchill who said, don't let a good crisis go to waste. Right. And so what happened was, you [00:09:00] know, we, our, our organization was on the old SMS, revenue cycle system for 28 years.
And, and that's, that fall, we were going live with our revenue cycle, with Epic. And so 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 EDW. Now we're going to come to a place where our data is really not going to make a whole lot of sense.
And so. I said, typically that happens after [00:09:30] about three months after you, you switch over systems. And, and so my prediction came true when, there was a board meeting, after three months we went live with our revenue cycle and because we have different reporting groups, Reporting on different things, but using the same metrics, our, our, CFO and COO kind of presented, same metrics, but with different, a little bit of different numbers.
And so that's where, you know, I got a call that night after the board meeting saying, Hey, you know that thing that you talked about maybe doing data governance, Maybe we need to get that going. And so I got [00:10:00] the thumbs up on beginning about Goma and it really, that's the Genesis of our strategy because as a, as a technologist, you know, I'm probably not the best person to really, you know, to, to, you know, set up the strategy, but also, really drive towards this.
What I need is a coalition of the willing, so to speak, to be able to bring the group together and making decisions together. And so, 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 use a through [00:10:30] your roadmap to be able to highlight, whether, you know, it was a technology need, whether it was the, the capability needs from a data literacy programs or different things that we need to have in our organization.
We use that roadmap and got the blessing from the data governance group to be able to lay out that strategy. And one of the key things that, you know, that, that we needed was, that's part of my assessment. One of the things I looked at is, you know, at that time we had about, eight plus different reporting groups, 12 different source systems that were [00:11:00] reporting off of.
but the big chunk was we had 60,000 access databases that acted as a quote unquote data warehouse for the organization. And so we, it was not a sustainable model. And so one of the things I, I partner with one of the reporting groups has to be able to 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.
They were, a ton of, [00:11:30] opportunities around how we not only collect the data, but how do we report off of it? So, if you, if you have atypical reporting, individual, as requests are coming in, they're spending, around 30% of their time hunting and gathering data. Trying to normalize it, put it into this access database for, cause we didn't have a, a system wide, whether it's a SQL environment or different things, to be able to provide that, that data, normalization.
So they use an access database as well. You know? So if you look at, the demand at the time, roughly it was [00:12:00] about 11 to 12% a year over a year that the demand was growing. And 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, the data to say, Hey, you know, that's probably not the best strategy because you're one, we have to hire two people soon after that, we're going to have to hire more and more and more, but we're going to be repeating the same process and getting the same value out of it that, that, that we're having issues with.
And so that's part of the data governance journey. we did a [00:12:30] commission study to look at. what type of a data warehouse capability that we wanted to go after. And so, you know, everybody knows at that time, Epic had, their own capability around, their, their data warehousing solution. And so we, we compare what was there and where we're at and what the marketplace had.
And one of the things that, that I've always done as a analytics leader is. You know, I don't want to spend a dime of our organizational money on something that has no value. So one of the key things that we focused on [00:13:00] 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 first 60 days, the 12 months.
And so that was one of the things that we look for. And when we looked at, at that time, the marketplace, not only the, the. The vendor who knew how to do improvement, whether it's care or costs, we both need, and not only the expertise, but [00:13:30] also the, the technical capabilities. And so when I looked at the marketplace, health catalyst as a vendor that we ended up going was had not only the.
The, the people and experience and the knowledge, but also, at the technical alignment that we were looking for. And so it was a, after a vendor selection, we went with health catalyst on their care variation improvement program, and also standing up their EDW platform. And I would say the key things that aligned with, their organization, not just the technology, but [00:14:00] ours is really.
That, that culture of improvement that really resonated throughout, their organization and their technology as well. So it was a, it was an easy match. And so, and that was about four years ago or how long ago was that? No, for we, we, I believe started that project, in 2014. So about five years ago now.
Bill Russell: Okay. Alright. And alright, so you you've, you've given us a lot to go off of and, and I'm going to, I'm going to end up asking questions around strategy, architecture, [00:14:30] operations, and really around innovation around this program. But you know, it's a couple, a couple things just jump out at me. One is, you, you did your own back in 2012, you did your own process for evaluating the system.
I know that HIMSS has their framework. Now, if somebody were starting this off today, is that the direction you would point them with that? The HIMSS framework for evaluation?
Chris Harper: I think the HIMSS framework is good. I believe at that time I used the advisory board, had a pretty good [00:15:00] framework around the maturity model.
And the reason that that resonated with me is really, You know, at KU our organization was already very savvy about using data to make good decisions. So, we had a very, data-driven culture that, that was an easy opportunity for us. And so what, what advisory board, framework data's really kind of had difference capabilities, whether it's the data literacy, whether it's the, the, the, the [00:15:30] technology alignment from a BI perspective, what have you. So I really resonated with where we were at. And so, that's how we adopted it. I think the HIMSS model is. It is very broad. It maybe a little broader than, than what, what we needed at the time.
And so we, we use the advisory board and we've modified it for our own internal uses. So it's not exactly as how advisory board drew it up. But so we use the, the, the kind of the model that. Went down to probably the next level layer of [00:16:00] details. more than what hymns, analytics, maturity provided.
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You seem to be communicating that each organization's culture and data needs and maybe [00:17:30] 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?
Chris Harper: Yep. That's correct. And then you also have to, look back and, and evaluate that every year.
Right? So we essentially developed this three year [00:18:00] roadmap to kind of guide us where we need to go, whether it's with funding, with, additional. Opportunities. You know, when, when, when I started, that journey, 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, improvement except for, 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 [00:18:30] and really 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, to really understand where your starting point is, and then really. A few weights, 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 it for 12 months, but if you're not showing your incremental value and [00:19:00] how you're.
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 it's a really, we looked at what are some of the five questions and then, quick wins we can have. And so, to use an example, at that time, our heart failure readmission rate was, was pretty bad.
So we were around like 22 points. Eight right around there. But we had a young physician that we just hired to help drive that program. And then [00:19:30] they've already had an established, kind of a care improvement group that was starting to dive in. And so that was an easy 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 they needed, which was the technology and the data.
And so. Yeah, we quickly partner with our physician champion and nurse champion since they already are focused on improving from a change management perspective and care delivery, [00:20:00] you know, within the first, you know, six months we got the environment stood up, really hyperfocused on it, develop what we call our analytics applications.
So they have the data at their fingertips that that day defined. They need it. And once we got that go in, it was an easy, again, easy lift for, for us, but we had a. You know, a clinical partner that was really willing to help. And so after about 12 months of actually, having the analytics application that the tool and the care team to really use that to improve, [00:20:30] we went from 22.8% down to about 18 and a half percent.
And so that was a tremendous improvement within the first 12 months. And then when we back to and measure, you know, what was the cost opportunity, not only, you know, better care for our patients, but also. what was the opportunity from a cost savings? really it was about half a million or sorry, $800,000, cost reduction opportunity that we saw.
and so that was an easy thing for us to, to really hang our hat on. So really, you know, within that program and a [00:21:00] couple of others, we already, pay for the investment we've made in our EDW structure. Yeah.
Bill Russell: So, a lot of our users like to ask me questions around, or want me to ask questions around budgeting?
So you, you sorta 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, care to acknowledge. the data definitions within organizations is, is, is a challenge. So that's sort of a, an impetus for a get moving.
Now, did [00:21:30] they. Like throw $20 million at you and say, make it happen. Or did they say here's, here's a, here's a little traunch 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.
Chris Harper: So it was more the latter, but little, little, less painful.
So I, I, you know, we, we built out our budgeting is, you know what, we'll let. What will it take to get us going? And luckily we had a vendor who was willing to partner with us, and, [00:22:00] and, you know, kind of structuring their contract in a way that allowed us to really kind of quote unquote pilot and test drive the capabilities.
And so, so we initially structured it in a way that. You know, within six months and 12 months of getting the project kicked off, how do we budget for those pieces? And then, once I was able to show how, whether it's, you know, rev regeneration or cost reduction or, or quality improvement, I always partner with our CFO and the financial team [00:22:30] to have a way to measure that out from a cost perspective and then show it.
You know, the, the, the, the value that we were adding once we met in the lie. And so when we make sure that the, the data governance group through that, that, 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 value that you're bringing, it was an easy sell.
So then we, you know, rather than going year after year, then we built out a kind of a multiyear budgeting to be able to kind [00:23:00] of grow the capability. And then the other thing I did is, we, we not only from maturity, but also tier steps are our investment strategy as well. So typically, really.
Where you want to invest a year. One is getting the foundational pieces in place. So we call that the, the operational reporting, investments. That's where you have, you're not on the EDW, but your, you know, data visualization tool and the data management and kind of the business intelligence teams to be able [00:23:30] to use that information.
To develop these, with their stash boards or reports or whatever. And so we really hyperfocused on that investment, 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 has.
The other thing was that we always hear is, you know, when we get our analytics rise going, they're like, okay, when can we [00:24:00] 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 talk about those things. But I don't understand that it takes upfront investment from getting the foundation built before you can really make an impact and those advanced analytics techniques.
And so. So we had a, it's almost a five year program where, so we're kind of at the tail end of that now where, so we, we invested into the operational reporting foundation, so that, that really, solidified, [00:24:30] 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, it took a little. I want to say a pivot. as part of my role as well, too, where if I partner with, we have a group internally called, lean promotion office led by, one of our physicians where that group is using the lean six Sigma methodology.
sorry lean, Toyota management systems to be able to do process [00:25:00] improvements, incremental improvements. And so, 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 more, more focused on the technology strategy and the data management strategy and on the ID component of it.
I always look for a champion who can really partner with me in this journey. And so, you know, back five years ago, partnering with some of the physician champions, driving the care variation [00:25:30] today, I have a, lean improvement. Group that I partner with to be able to really, you know, use information technology is 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.
Bill Russell: 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. which, which wins did you target and which ones panned out and which ones maybe. Either took too [00:26:00] long to pan out or so, which were good, quick wins to shoot for and which ones didn't really pan out for it.
Chris Harper: So again, every organization is a little different than their need, but we're, we're hyper focused on patient satisfaction and quality. And so. The five initial, things that we tried to improve on as a quick one was, patient satisfaction. dashboard is kind of what ended up being, but so, so we, we are hyper focused on understanding what our patient needs are through that, press Ganey.
[00:26:30] Opportunity. And so, but it was such a cumbersome, some effort to collect that data and send it out every, I believe it's every month, or, or now I think we've gotten better than every month, but we send out this 400 page PDF. Every providers and every, you know, nurse managers and so forth. And then they have to kind of really some through that PDF to understand where their patient satisfaction was.
And it was a static data, so they could look at it and get the additional, [00:27:00] granular information, but they have to actually go somewhere else to be able to get additional information, if that additional question. And so, we really focused on that because we knew that was, Not only manually and tests it to, to produce that report, but also on the consumption side of really adding value.
I think people are using it, but. Using it differently. And really it wasn't able to answer a lot of the questions that they probably have. So for instance, if their patient satisfaction is going down in certain, you know, things like, nurse communication, like they [00:27:30] have a lot more questions about it.
What do I need to do about that? Well, so what we focused on as one of the quick wins was, you know, 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, As your typical nurse manager might have questions about their unit on their patient satisfaction.
We served up different components of the data to be able to answer those questions for them. And so that added a quick, quick, quick win and quick value. So, you know, getting the data [00:28:00] in developing these analytics tools to build, to help me answer those questions. And then I mentioned about the heart failure one again, we're focused on quality improvement.
There were things that we look there. substance DOD was the other one that we were able to, get it up, rather quickly and start really iterating. And that's that's the other component is whatever you develop. it's not going to be perfect the first time, so you have to apply that agile methodology.
And so, you know, so we do a, just like a software development [00:28:30] effort. We do releases, every two to four weeks of that. You know, if, if you know this, patient satisfaction, application analytics application was stood up. It doesn't have everything that everybody needs right away. So then, we commit to, having optimization enhancements every two to four weeks so that the users, when they ask for some things that, you know, it might, they might not have it today, but they might get it in, you know, the next release or maybe really fast for that.
Bill Russell: Sorry, I keep muting because the [00:29:00] guy across the street decided to trim his trees right now with the chainsaw. So this seems to be no one wants us to record. So how did you measure it at first and has that changed in terms of how you measure the effectiveness of your program over time? so I think we've, so we always try to add value.
Chris Harper: So, We always tried to measure in terms of, of opportunities and saving. So we always measure, you know, we invested this much into it. Are we getting the value out of it? So we measure [00:29:30] it from a kind of an ROI type of a, assumption again, you know, if you talk to any organization who does an ROI model for their data analytics, it's, it's never.
Yeah, a hundred percent meaning that, you know, CFO's walls as well, that it actually take dollars out of my bottom line by investing this wall. It's never an exact science, right. So meaning, you know, it's just the technology investment in people investment, but ultimately it's how you use the information to drive change and [00:30:00] progress.
And so, and that's where today, you know, one of the things that I, I really appreciate partnership with our. Our lean team is that they're, they do kind of a three to one investment returns. So, they have to show three times the savings or improvements to be able to ask for another, you know, a dollar or an F or so forth.
So they really help us measure some of those progress value that we're bringing in and, and, and. You know, the hard thing is getting word that initial, big capital investment [00:30:30] hump. Cause once you get over that, your, your ROI gets a lot easier. but yeah, that, that for that organization, that's where they get really hung up on is man, I've got to invest this, you know, six, seven, eight figure investment upfront to get the value out of it.
You know, whether it's, you know, three year return five-year return. but you know, really, you gotta look at it as a long term strategy versus, you know, every year return kind of a thing. Yeah.
Bill Russell: All right. Well, in the last five minutes, I wanna, I wanna, we hit strategy. We hit operations. I want to talk, [00:31:00] architecture and, Oh, sorry.
Architecture and innovation. 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, other platforms as well. like, like Epic for operational reporting as well. walk us through, you know, how you think about, platforms within the data analytics, in terms of the [00:31:30] strategy and the architecture. How do you, how do you think about different systems and how they fit into the overall overall framework?
Chris Harper: No, that's, that's a great question. And, you know, my background actually, is, is before I came to kill you as really driving kind of the, the, architecture and what we call a capability modeling components, to be able to really put together a strategy, whether it's your analytic strategy or whether it's sure, you know, you know, [00:32:00] digital strategy or Epic strategy.
And so. But my background is, and to be able to kind of, develop this, framing of, of people process technology to really, you know, identify the need and the strategy. And so, from a, our overall data analytics from a, architecture perspective is, you know, the simple question I asked is, you know, do we want.
as an organization, you know, have ultimate control of our data. If we truly believe data is the, you know, our strategic [00:32:30] assets. I think there are some, a con economist magazine identified, you know, like, I think last year or two years ago that today data is more valuable than oil. And so, you know, so it's a simple question I asked where our data governance and our senior executive is.
Do we truly believe in that data ultimately is our, our most strategic assets. You know, do we, 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 invest into whether it's Epic version or the [00:33:00] others. And the answer was, knows, meaning that we want to just have ultimate control over our data destiny.
And so our architecture really lays out, you know, when we started a health catalyst, had the rights, from a capability perspective to really get us going where we need it. But quickly, we realized there are things that health catalyst is not good at, or doesn't even do. So for instance, we had an internal organizational need around, doing better job of recruitment.
So we were building a [00:33:30] new, a new hospital power. And one of the things we need it right away is there is no way that, that we were going to be able to hire people fast enough 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 the data and our analytic capability to improve our, our, our speed of recruitment and quality of recruitment.
And so I partnered with our HR team to be able to stand that up. The health catalyst doesn't offer anything around that. And so when we [00:34:00] initially, structure our relationship with health catalyst, we, we had the ability. So, from a platform perspective, we built it on our Microsoft SQL because we knew that, internally we had, a tremendous support from our infrastructure team who are well-skilled skilled, then Microsoft.
Products, but also we have a lot of, skills that are wrapped around that. And so from a technology platform perspective, we, we partnered with Microsoft quite heavily and then started to invest into that multi-year strategy. [00:34:30] and so health catalyst is really, what I considered a sister EDW environment.
We have our internal one that's on the same platform. but has a different mission that it serves. And then, same thing on the research side. So right now we're talking about how they would do additional research, using the same platform approach. so we have a consistency around our technology infrastructure.
But we can spin up another, kind of 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 [00:35:00] a service bus model. something I learned as a software guy, a while back where, you know, all of that's great, but you have to have a cohesive data management.
A 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, at the end of the day, it's, it's not just this one, you know, thing that health counts provides, but als the additional needs [00:35:30] organization have, and then also Mary's and obviously the, the Epic capabilities as well. So today we haven't had the need to invest into the caboodle strategy yet, but I always tell our organization that it's not. Yeah, but when we need to invest into that, because there will be a need where a business case, or a use case that will we'll say, Hey, here's what I need.
And here's a value that'll generate out of our, our Epic caboodle strategy. So today we haven't had a NICU, [00:36:00] a nice to our other Christmas, but, but I think quickly, we're going to show that we're going to have to invest into, the, the epics caboodle strategy as well. Cummins come in soon.
Bill Russell: Yeah. So, in the last couple of minutes here, let's, let's sit on innovation real quick.
Where's innovation going to 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?
Chris Harper: Oh boy that's a tough question. I, I think, I think [00:36:30] if I step out of our healthcare, For a second and look at where technology in general has come and it's going.
So I remember, you know, having done this, you know, back in early 2000 and 2002 with my analytics career. Really back then. it was all about horsepower computing cost of storing data, because back then, you know, spinning up a server or data in Burma's shoes is [00:37:00] 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 partner with organizations like Terra data, you know, Informatica to really help us to kinda, build that capability out.
Today, you know, a lot of it is really commoditized. So whether it's, you know, AWS from Amazon or Microsoft Azure, you know, there's a way to get around the infrastructure, capability challenging. Cause the innovation has came so far in the, the. [00:37:30] You know, server and stories and all of that, even how you visualize data, whether, you know, one thing that clicked for you was really, was good at it is they have an in memory capability to be able to quickly, go through the logics that you develop when you develop a dashboard and back then, like nobody else has that kind of capability.
So they were really, taking the market by the corner and, and, and really driving the data visualization, capability. What I'm focusing on now is it's all of those things have became pretty much, you know, [00:38:00] standard functionality or, or commoditize from, from, storage and server and computing power.
What I'm really keen on is there's so much information and data, 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 learner capability.
[00:38:30] You know, I think the, the, the, the, the winter hasn't been really, being clearly defined. And so what I'm doing is just kind of watching and seeing. What opportunities 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 I had to apply some automated way versus just keep hiring more people to do.
Some of the manual work. And so the key area that came up was our testing. So, [00:39:00] so we, we actually integrated and implemented automated testing with our Epic capabilities and that's shown tremendous, ROI within the first five months. So that's kind of where I think I'm really focused on is from a data and analytics perspective.
What are some of the machine learning and artificial intelligence I can really. Help you automate, but also, help you focus. Cause right now, 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 [00:39:30] capabilities within, ML and AI, and that's where we can really, make advancement and innovation in healthcare.
Bill Russell: Fantastic. Well, we're close to the end of our time. I love your, is that a, Florida ceiling whiteboard in the back?
Chris Harper: It is. Yeah. So, you know, hopefully, I don't know if you can read that, but that's kind of our, our, our, so we, we try to convert all of our, office space into multi-use functions. 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 [00:40:00] resource as well.
Yeah. And so if you have, so we converted all the offices into having a white board of a wall or a whiteboard space. And then if my team's needs, you know, have a meeting with less than four people, they can use my office as part of that as well.
Bill Russell: Yeah. I, I made this mistake. I actually did that. And, I couldn't reach above like six foot because I'm not that tall, but I had a guy on my team who was six foot eight s o he always took the top sections.
Chris Harper: That's great.
Bill Russell: So, Chris, thanks [00:40:30] for coming. coming back on the show, I really appreciate it. Is there any way people can follow you? Social media, LinkedIn?
Chris Harper: you know what, I don't do enough of social media. I am more of maybe kind of an old school as a technology guy. That's kind of interesting, but, you know, if anybody wants to contact or reach out, you know,happy to work with them. but yeah, so, I mean, I think on, on my LinkedIn, I do have a public profile and if they want to shoot me a message, happy to have a conversation, but, yeah, I don't, I guess [00:41:00] post anything. Cause I just, I'm just kinda focused on, the work I'm doing here, but I'm happy to connect with people.
Bill Russell: Cool. Well, I love it. And I appreciate you took, took my call a while back and helped me out with a client project that I was doing and a really helpful, I mean, just the amount of experience you've gained over those six or so years of implementing that, that program there has, it is really, I think, valuable to the industry.
And I appreciate you coming on the show to share it.
Chris Harper: Yeah, no happy to do it. Thanks for having me though.
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