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Today on This Week Health.
(Intro) We're gonna solve the supply and demand challenge, the imbalance there because everybody in your organization will become data experts because all of the, in the information and knowledge is right inside the tool.right. Here we are from HIMSS:
Sure. I'll go first. So, I'm Govind Thirumalai. I'm the vice President of business Intelligence at Parkview Health.
We are located in Fort Wayne Indiana. It's like the northeast corner of. Indiana, so we cover northeast of Indiana, southwest of Ohio, those Michigan, Ohio, and Indiana
across three states. No, that makes it fun. Across three states. Yeah, it is. It's people don't understand the complexity of going across various states.
It's a lot of fun. And Terri, what's your role?
I'm Terry Michael. I've been with Clearsense for about two years now, and I am a data governance advisor. Long story short, I've been in healthcare data analytics for 35 plus years, and am finally solving the problem of the supply and demand imbalance where we just can't keep up with the need for information.
Data governance is the silver bullet there. So that's my role.
Well, I do want to. Unpack data governance. So when I was cio, we did a data governance project. My team wanted to string me up. But I'm like, look, we the quality of our data the definitions of our data, I mean, just so much of it was just nebulous when I came in and they're just, well, we can get it from point A to point B.
I'm like, yeah, but we can't use it when we get it to point B or they don't know what it means when we get it to point B. Tell, what does data governance mean? Give us, first of all the structure and definition.
Do you wanna tell them our six questions, how we define the scope?
Yeah, I mean, so for us so that data governance, no, jump into whenever you want me to cross connect or something.
But no, b we are focusing on six work streams. I mean, When I say we, Terry advises us to focus on six work streams. We focus on the data domains. We focus,
this is like a test. What are the six, right, exactly. Terry's making sure. So what are the six? So
data domains, master and reference data. Data quality analytics, community metadata, data policy and data policy.
Terry is forcing us to have a structured approach of solving this. Obviously data governance is, it's a big elephant, but she's teaching us. Start eating the elephant from the trunk or the tail, come up with a strategy, so that's what she's helping us.
You've probably experienced a little bit of the, you start with the data quality issue, but No, it's a bad definition. But no, we have that data in three systems. So which one's the best? So you're hitting all of those work streams, you just don't know it. So by, by creating a structure. Yeah. So we have people focused just on data quality, others just on definitions, others on, we've got this data in three different systems.
Why is it different? What's the best source of truth? So we use a very divided conquer type of approach to, to get at it. Involve a ton of people. And basically I'll sum it up like this. Here's what data governance is. We provide answers to these common questions. Where can I find what I need? Is it any good?
What does it mean? Where did it come from? Who's responsible for this? Yes. And what am I allowed to do with it? Think about it. The time that we spend trying to find answers to those questions in the data world,
well, we, we don't, what we do is we keep going to it and say, we need more people to do this.
And then the IT team comes to me as a CIO and says, Hey, we need to add another 10 people to our data team. And by the way, that was a consistent, every year it's like we need 10 more people because the demands are growing. And at the end of the day, the supply of people is going down. So
it's, so think about how we've solved that problem with our other strategic assets.
Cause we've been down this road before with hr, with finance, with supply chain. What happens is it starts showing up in everyone's job description. That's how you scale. So we're going down that same path.
I mean, you just gave us that set of questions, but the hard thing is when it. It starts to get used out in the department or out in the hospital.
How do we make sure that they have access to those answers?
So, I am a people process, first type of person, but tools are obviously important. And why I came to Clear Sense is because we are combining not just your data. But all of the institutional knowledge about that data as well. In other words, the answers to those six questions will be in the platform alongside the data.
So it becomes a learning platform. The more people use the platform, the more they, their data literacy will mature. Because if I'm looking at physician name, I'm going to see, oh, it's the name that's on their medical license or, oh, it's their legal name, or, oh, it's their preferred name. It's going to tell me that I don't have to phone a friend to find out which name is called.
Let's go down those six paths. Cuz when you're describing that, I immediately think metadata, right? So, all those little pieces of information we pick up all along the way. The the interaction at the front desk where they go, oh no, I know it says William, but I go by Bill. That gets picked up and that gets put into the system and at that point I'm dealing with the EHR generally, but somehow that information is flowing.
It needs to flow out of there and get to our call center. And our call center needs to call me Bill, not William. Because otherwise they're gonna say, William, I'm gonna say, call me Bill. And that's the, we hear that all the time from people saying What's wrong with the experience in healthcare.
It's like they asked me the same question over and over. So this is an only touching. The analytics side and the clinical side. It's also touching the patient experience side as well. I'm not sure I have form a question there, which is like, my job is the form of question. No, I but I do wanna go down those six areas again, given to me again.
So it's data domain, data, domain data policy, master data, and master and reference data. Okay. Metadata analytics community. Data quality. Data quality. Yeah. That cannot be the least, but yeah.
Alright, so let's start with the first one. What does it mean?
So before you can assign ownership and responsibilities to something, You have to divide it up and say, you're in charge of this. You're in charge of this. So we use a data domain structure to group data elements together. Yeah. So that we can assign it to somebody.
So you are gonna govern patient encounters. You are gonna govern medications, you're gonna govern labs, you're gonna govern images. That's all it really is. It's a grouping so we can assign ownership. Well
to your initial question, right? Data go. As an IT leader, as a BI leader, data governance is not a BI issue.
Data governance is not a CIO's issue to solve or something. What Terry is saying is, we group this data together, these data domains together, so the ownership is spread across the organization. When Terry was talking about, when she was giving an example about physician's name, so it's just not a bi team to solve.
It's more like a let's have human resource into this mix. Let's have your credentials into this mix and what the Terrys are making us do. The right thing to do is to make sure, hey, we are all owners in this together or something. I don't have to take ownership. We all ownership, we all take ownership together or something.
So in that process of identifying the data domains and assigning ownership, is that part of setting up the governance?
Sure is. And it's ongoing. It's not one and done. The ownership and assignments will change over time as corporate initiatives change. Sometimes you might want to bring things together under one leader so that you can get some horizontal.
Things going on. Yeah. Other times you might want to split them up, like maybe medications is too big, maybe we'll chemotherapy out because it's a good bit different than the other meds. So it's not a one and done. You start and then it's constantly changing to meet the needs of the organization.
But it is just ownership
policy. What's the next one? See, I can be taught data policy. Does that belong at the higher level within the within the data governance, or Is that handles, where is that handled?
So I mean, We go with, whoever is responsible for data policies, right?
We work with our CSOs, we work with our complex officer. So whoever is there to dictate the policy, again, going back to the initial comment, the rightful owner can own that information or something. The data policy, it's not like an ID job. We had to make the right folks take responsibility.
Is this where we're defining like, this data means like length of stay. So always one of my favorites, I'm like, well, length of stay, that's length of stay. And people would look at me like, no, you don't understand. It could mean this and this context, this and this kind. And I was like, okay, well how do you determine what is that?
Does that come into policy?
we do that in the metadata data. Okay. Here's how I like to define data policy. The governance gets a bad rap. This is not about red tape, and this is not about slowing people down. You can't give people access to an asset that's strategic unless you give them guidelines.
So this is about helping people find answers to that. Am I allowed to? What am I allowed to? Exactly. Got it. And we don't want them to have to pick up the phone and call it's IT security or call privacy every single time. We want user friendly rules out there. So for example, we have to secure site data separately.
Always have, but has anyone written the definition of how to actually find site data in a data warehouse? Which diagnosis codes, which medications, which procedures, which clinicians, no, we've never done that. So in data governance, we come in and enable them by helping them define things. So we are in the policy space.
We're all about. Clarifying, just clarifying the proper use of this asset, not slowing it down.
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now, back to the show.
Sure. So we're done through two, let's go to
metadata. That's your length of stay example. Yeah. Metadata. Yeah,
so, so that's where we're defining the different Information that needs to be stored about the data so that we can understand what the data actually is telling us.
I mean, yeah, you are spot on. Yeah. That's why we, I mean obviously metadata is like the data of both the data. So, right. So what does it mean in the long run? I mean, Terry and I, we were just talking about it. Length of stay is a very good example. If you ask eight different people, they'll give you 10 different definitions on length of stay or something.
Eight people, 10 definitions. Exactly. And that's pretty accurate. Exactly.
So this is where. It's not like, no, we are making the call, we are pulling in the right for owners and it's more of a no, let's all get together. This is what, how we are going to define metadata, for a particular data element or something.
Do we agree to it? I mean, it can have 10 different definitions, but do we have a meaningful name here? Okay, as finance is gonna measure length of stay differently, then a finish year, it's like, is it like if the birth patient is over there over midnight, does it consider one day or is it like button or something?
What is considered length of stay? So we cannot have eight different definitions, but are we clearly documenting it?
That's the key. So it isn't about getting to one necessarily. Getting to one definition per use case. Obviously right, but here at Clear Sense, we believe that there are multiple lenses and there are reasons for multiple definitions.
It's our job and data governance to make sure that each definition is owned and documented properly so that people know when to use it.
But when you present that data, you want a
definition's right there with it, right alongside it, right alongside of it. It's all right in the platform.
You can pull it into the reports, the dashboards. That's the key. Put a little footnote. Yep. This one includes your in-house population. This one doesn't. Yeah, exactly. It's all about that.
So that's three What
we'll go with the master in reference data
where your data's in multiple places and it should be the same and it's not.
How do you even identify them?
I love this. Inventory. You walk into any healthcare organization and say, where is all your data?
And they will say, I don't know exactly.
We have a list. The help desk system has a list, but it's not really that up to date. So again, by assigning ownership to the various data sources, metadata is now part of production change control.
It's not optional anymore. You will keep an inventory of where your data is and the master files. Provider, master, employee master, patient master, we know exactly where they all are. And where they're being updated so we can identify specifically a client's master data problem pretty early on with a basic inventory.
What's the source of truth then?
Depends on the use case. So if I'm building a dashboard for some legal entity, I might want to use the provider's name that's on their medical license, where if I'm building something for find a Doc, an external lookup, maybe I want to use their preferred name.
It is very use case dependent. Multiple
lenses, sorry. And this exercise gives us to identify who wants it, to your point, who wants the definition? The exercise of identifying the master data, gets to the point who should be the owner of this? So the organization now knows this is the team, or this is the leader I have to reach out to for a specific data element.
This is no longer like, a nice to have. This is a must have, especially given this financial pressures that we're looking at, especially given the I mean, look, just if it was quality alone, it's a must. Exactly. Now I missed two more. Areas, what were the last two
quality? And then we have this special
Yeah. This is more like the whole, again, correct me if I'm wrong, this is where the users of the data,
There are like there are teams where are, they're like just happy with basic Descriptive analytics, but there are teams who wants to get into predictive and descriptive side of it.
So the analytics community, besides the hunger across the organization, and no, it gives the right kind of analytics to the right kind team.
So you've heard the term shadow it. Right.
I've also heard the firm self-service analytics.
There you go. Okay. So bottom line is we shouldn't be trying to shut that down because we have this supply and demand challenge.
What we need to do is enable more of it. So we form an analytic. Community. We're not changing where people report. We're not necessarily even changing what they do. But if you're the data person in the pharmacy, you now belong to a community that can teach you things that can maybe, oh, they already have that report so you don't have to duplicate it.
Or, oh, maybe we only need one census report in our organization instead of a hundred. Right? So when you put them together as a community magic content,
When we had our EHR go live and our implementation, we first of all, we had to inventory all of our old reports, and then we had to create 'em. And from the moment you do that implementation, it like just keeps
So you never shut anything off, do you? You never shut anything. No, you don't.
And eventually what you end up doing is a, an exercise where you're like, all right, start shutting 'em off. And.
See who complains. Yeah. Yeah. So in, in the analytics community, we also have a policy where all production solutions are evaluated on a twice a every two years.
Is it still valid? Is it still being used? Is it still right because you have to clean this stuff, but the community. Decides that you bring them all together and they start saying, these are things we need to be doing as a community.
Do you get pushback when you're bringing this into an organization where they go, look, I have a full-time job.
I can't possibly and how do you get them on board?
We rely heavily on. The other areas that are also managing strategic assets who also push this out across the organization, like human resources. Do you have HR things in your job description? Yes. So do i. I don't work in hr, but I've, okay. So we rely heavily on.
Supply chain, finance, HR to say yes. When an asset becomes strategic, shows up in everyone's job description, this is how we scale. Every person takes just a little piece. For example, you're the registration clerk. In admissions, we're gonna give you patient names. You're gonna own patient name, and in your own time, you're gonna find out how they handle it.
In the er. When a patient comes in with no identity, is it Jane Doe? John Doe. What do we do? What do they do in the nursery with newborns that aren't named yet? Baby boy. Baby girl. What does it do when they're testing patients? Do they use Mickey Mouse? Like so She's just a registration person full time.
She just needs. Just patient name, just one.
So social security
number could be somebody else. So she's growing because she now has some horizontal knowledge of patient name across the organization. And she did it in her own time, made a few phone calls, sent a few emails, and she owns patient name.
Her name is attached to it. So, yeah, that's how we do it. And
you do that with all key elements of the patient. For cost support.
I know I, I quickly want to take a couple of steps back. When you were asking the question about do you get pushbacks or something, and this is not a first rodeo, we tried data governance like this is our third or fourth crime, we get pushback all the time.
And then, I was thinking we need industry leader to help us in this engagement. And that's how Terry came into the picture place and came into the picture. And Terry's heavy healthcare knowledge complained with a clear understanding of the data governance. She took the approach of, you know what she calls it as a data asset management training session.
So what she did was she went around the organization from managers to directors, VPC level. She trained them on what data governances. If we didn't know, we didn't go around and say, Hey, we need to implement data governance. This is how we are gonna do it Instead, she said, why? Why? We need data governance.
So, and then very
standing. Exactly. Cause it's an asset, but you don't know where it is and you don't know if it's any good and you know who's in charge of it and they're all like, you're right.
Exactly. So it connected, the questions were tailored to them, so it connected to their day-to-day problem.
And we have a strong buy-in. Yeah. There is a certain kind of pushback, but they know at the end of the day we are going to help them with their day-to-day. Or like, what are the strategy? Strategy they have, they chooses what they fear is from the data side.
So what's enabled, now you have the clear sense platform in place, put discover this model.
What are you able to do now that you weren't able to do
over a little while?
We are again, they're a very phased approach or something. Carrie has a four phase approach.
The first phase is again foundation. The foundation this is like a quiz for you. Exactly right.
She's putting me in something
and no. I think now the foundation and then the next one I know the engagement and adoption. The second phase is awareness. Awareness. So, so at least now the entire organization is available of why we need data governance, why we need it, and again, they have like structured approach all over or something.
So now we have identified the folks, we have identified our council chairs, council members. We have a clean framework to it. And I mean, these guys are not, in fact, they're volunteering to be the council members and stuff like that. The training helped us. Not
a dirty word anymore. Now everybody wants in.
So, Terry, I'm gonna give you the exit question and it is, we've done a couple of interviews with clear sense people are gonna go, oh, it's intelligent interoperability. Oh, it's a data fabric. Oh, it's a foundation for clinical research. Oh. Give us an understanding of. All right.
This clear sense platform, what does it enable for a health system? Because it's not one thing, it's a lot of things.
We're gonna solve the supply and demand challenge, the imbalance there because everybody in your organization will become data experts because all of the, in the information and knowledge is right inside the tool.
So if you can read. You can know your data inside and out, right? Plus all your data's gonna be there for you, so you don't have to go hunting around for it. So I'm gonna say Clear Sense is the platform that will elevate your data literacy across the entire organization. And it is designed to support most every use case because.
It's not just a single use case. We have a thousand of them a day. Right. This is about enabling more people to work with data. That's what this is about.
Fantastic. Thank you. I appreciate your time and the education that you provided me. I, it really is fantastic. Thanks for having Thank, appreciate it.
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