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When the pandemic struck, many health system data teams hit the panic button very quickly. How in the world were they going to do what was being asked of them? How could they deliver care as quickly as possible? What role did data fabric and data virtualization play in this time of uncertainty? How did they help to simplify and integrate enterprise-wide data management and delivery across a choice of endpoints spanning on-premises and multiple cloud environments? How did they help to provide seamless access across multiple clouds, data centers and even edge systems?

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This transcription is provided by artificial intelligence. We believe in technology but understand that even the smartest robots can sometimes get speech recognition wrong.

thanks for joining us. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of this week Health, A set of channels dedicated to keeping health IT staff current and engaged. Welcome to our briefing campaign on modernizing the healthcare data platform with CDW Healthcare's leaders in this space, Lee Pierce and Rex Washburn.

Today is episode three, how data fabric and data virtualization ease the panic of the pandemic across health systems. This podcast series is going to culminate with an excellent webinar panel discussion with experts talking about how to modernize your healthcare data platform, the right fit for every unique health system. That's gonna be on Wednesday, December 7th. Check out this week, and click on the link to go ahead and register. We wanna thank our sponsors, Sirius, CDW, and Microsoft for making this content possible now 📍 onto the show.

On this discussion, I wanna play dumb because things have changed a lot since I was delving into this. So somebody helped me understand data virtualization and data fabric. What are these things?

data fabric, it's, it's both an architecture and a software approach. Different vendors are labeling it different ways, but we're just gonna sort of focus on logical fabric because I think it's the most realistic and, the thing that. Organizations can implement right away. But it's the unified layer where we leave data, where it rests or where it's most performant, and then we query through the fabric to all the divorce data sources and knit it together in one place.

There are other concepts pulled in there like unified governance and unified security, unified single pane of glass for all data. But all of those things come together to basically say, leave the data where it sets, query directly through it, and drive for optimization speed that way.

All right. So Lee help me to, help me to understand it through use cases. So I hear those words and that makes sense to me. We're gonna leave the data where it is. We're gonna have a unified security framework. And those kind of things that makes sense to me, but help me to understand it. With use cases, how are we using this in healthcare?

one of the best use cases for the application of data fabric and data virtualization specifically, is for health systems that are in the process of doing mergers and acquisitions. Because what happens in those instances is one of the first questions. Is, how can we get all the data together?

And many times it's almost immediate that they want that to happen, thinking that we can just snap our fingers and be able to get that data from one health system and another together in one place, and be able to do analysis on that in a meaningful way. Well, actually it is almost as easy as snapping your fingers compared to what we used to do, where we would move that data, as Rex talked about from one health system.

Have a database where you can put that into another. With the application of data virtualization and a data fabric, you can do that very quickly. specific healthcare customer that's in the process of doing mergers and acquisitions is an epic customer and just the clarity and caboodle databases that Epic provides that is standard across Epic customers.

There is no way within the Epic ecosystem to be able to unify the clarity and caboodle instances. And so what this health system is doing is using virtualization technology to be able to to sit on top and truly be able to query data across the integrated virtually without moving the data clarity and caboodle instances that all of a sudden they were able to do the analysis very quickly that they're being asked to enable. and they're seen as a bit of a hero because it's happening quicker than they were ever able to do that historically.

That is a fascinating use case and I think one that's gonna resonate. So we have an opportunity to be pretty nimble here, right? And that's, that's what the cloud is all about. So we can move a lot quicker to speed to value. We talked about speed to value. We talked about the. The agility of this of this environment.

Let me keep going through healthcare use cases. So I'm gonna throw out my use case. And my use case was this, in southern California, we couldn't employ all the physician practices, so they were all on different EHRs. And then they came to me and said, Look, we need a a single dashboard for providing physicians feedback on their performance.

And we had all this data, by the way, we need a single dashboard. We need ways for them to see their performance, their pay for performance, a bunch of other metrics. And so our clinically integrated network needed a common fabric. Now, you were talking about not exaggeration, a hundred different EHRs.

Even if they weren't distinct EHRs, they were different build to the ehr. And we, we start down this path and. It's challenging. It's extremely challenging because back in the day, back in 20 12, 20 13, what you're looking at is saying, Okay, we've gotta move this to a central repository and then start doing this, this kind of thing.

Talk through that, that use case. And we didn't talk about this ahead of time, so I'm, I'm getting free consulting from you guys right here. Talk about that use case. When they come to you and they say, Look, we have all this disparate, all these disparate data sources. We need to bring it together. We need to create a series of metrics, but then we also need to be able to tell the story, get that information back out to the clinicians so that they can do their job effectively. What does that look like in this environment?

It's, it's actually, as Lee was pointing to you, it's, it's sort of magic. Once you have the virtualization platform in place, I mean, it is simply the, the hard part is making sure we can connect to all those different EHRs, right? You gotta have a network somewhere where we can touch 'em. Once we have that connect. At that point in time, we're basically using very standard sort of database development thoughts of building views and unifying the data together.

But we are driving the queries down to those source systems and knitting them together. So if we're talking about I want a kpi, I'm not quite sure what's going to be the leverage point. That's, that's a, a tough use case in all data because we spend all the time moving the data and then we haven't moved the right data and could get to the use case.

So what we can, what we do is we get the connection and we start looking down towards where is some area that we're wanting to look at, Let's patient outcomes something for the clinicians, that dashboard you're talking about. Let's start pulling small num, small amounts of data up through the layers and unifying them across all sources until we have that one number. Now you might say, That number is great, but it's a little off. Or I need to add some piece extra data to it. The old days, we would smile and then we'd go back into our office and scream a little bit because now we have to redo everything we did before. But when we're talking virtualization, it's really just a matter of saying, Oh, you wanted these extra fields or this extra table.

Let me add that into the stack of views I'm creating, and I'll bring you back the data, the speed iteration. I've talked with a lot of different healthcare providers. Everything from pulling together covid data to unifying ehr, and that's really the magic that they saw was we can pull things together and iterate so much faster by not having to do the traditional data movement across all the platforms.

And then we iterate across data products. So get to the first kpi, what does it look like? And while people. Making that, making the visualization, building the dashboard, we can start bringing in additional data, right? So the, the speed, the iteration cycles go a lot faster cuz we're separating the speed of business from the speed of data engineering.

Awesome. So what are some of the use cases from the pandemic? I mean you mentioned the pandemic and mm-hmm. . It was amazing to me some of the conversations I had with healthcare organizations and they're telling me, Yeah, we're, we're pulling all this data together and we have demographic data, we have. They're using geolocation data and that kind of stuff, and they're saying, Hey, the, the spread of covid is more likely in this area than this area, so we're gonna do our drive for vaccinations in this area. I mean, there was just amazing work being done. what are some of the use cases that, that we saw during that time from organizations that were nimble and agile?

There's actually a one of the vendors, the virtualization vendors we work with, they partner with the healthcare organization simply because they had all of those different sources that you mentioned, and the time to get a dashboard out onto their website was months. I mean, it's standard development cycle when I'm pulling together a lot of data through virtualization, they were able to lower the data piece of that down to just weeks.

Now we see this pretty regularly where we can take a. cloud based MPP platform that has a data marketplace, let's say, where I can pull geo data, other data, that kind of thing, just swipe my credit card or add it to my account and pull it in, that's sitting over in a cloud somewhere, or multiple clouds.

Now I have my EHR data, I have my other data being able to just sort of rapidly pull that together without having to. That was the key element for, for folks. And once it's out there now, the added value of ml just auto ML that's native and all three major clouds that are out there that can be pointed towards that virtual layer to start driving even more insights.

So time to producing insight that can actually have a significant value to the populace is that's some, that's a place where that virtualization plays greatly. And you can't do it other ways without a small army of developers.

the pandemic certainly was a point of I think many data teams within health systems hit the panic button very quickly around how in the world are we gonna do what is being asked of us as quickly as possible?

Many were better prepared with their legacy data platforms that they had in place to be able to pull some of that together. But we as Rex said, we have examples of customers that then were able to utilize data fabric to be able to do that even quicker than before.

one of the other components of even the application of bringing many different data systems together, either during the pandemic or the hundred EHRs. Like you started with Bill and, and you asked the question, you still have to apply there's the technology component of it, but you still have to apply another topic that we'd like to reference right alongside modern data platforms, which is data governance.

Mm-hmm. , you still have to be able to understand the data that you are wanting to bring together, where it's coming from, the definitions around it, so you know if you're actually joining the same data and, and is it of the quality that you need. So that's something that has also come out of the pandemic is yes, there is the application of these modern data principles and associated technologies, but there's also then the importance of just simply even defining during the pandemic.

What is a COVID patient and how are we gonna calculate that? And and, and do we even have that as an, as a data element within our systems? Because it wasn't even a thing prior to a certain Right. Point in time. There was no lab code associated with Covid prior to for many of these health systems.

And they had to go back to those fundamental definitions. And, and that's really applying data governance principles in a meaningful way. Plus the technology just made it possible for organizations to be able to quickly, as quickly as possible, be able to get the data that was necessary to present to their the command centers and that were stood up because of Covid.

Well, fantastic. And Lee, I appreciate the segue cuz we're gonna talk about clean complete data and we're gonna talk about data governance. in the next couple of episodes. Gentlemen, thanks for your time. Really appreciate it. Thank 📍 you. Thank you.

What a great discussion. I wanna thank our sponsors for today's Sirius, CDW and Microsoft for investing in our mission to develop the next generation of health leaders. Don't forget that this whole series ends with a great webinar on Wednesday, December 7th, Lee Pearson, Rex Washburn will be joining us along with Jared Nunez, Executive Director Informatics and Analytics at Memorial Care. We're gonna take this discussion one step further by including you and your questions. So go ahead and register at this week, The link is in the top right hand corner and don't forget to drop your questions in the form so we can make sure 📍 to cover. In the webinar. Looking forward to that discussion. Thanks for listening. That's all for now.

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