8. Healthcare Interoperability: Part 1 (Russ Leftwich)

Speaker 0 00:00:02 Welcome to data points, a podcast by InterSystems learning services. Make sure to subscribe to the podcast on your favorite podcast. App links can be [email protected] I'm Derek Robinson Speaker 1 00:00:16 And I'm Adam cupola. And today we'll chat with Russ Leftwich, senior clinical advisor for healthcare interoperability at InterSystems about modern healthcare interoperability. Speaker 0 00:00:25 <inaudible> welcome to episode eight of data points by InterSystems learning services and much like last episode, I'm joined by a fellow co-host at online learning Adam, how's it going? It's going well. Thanks for having me Derek. Cool. So today on episode eight, we're going to be interviewing Russ left, which, who I'll have Adam introduce in just a moment, but first a little bit of housekeeping at the beginning here. Uh, first of all, we hope you guys have been enjoying these episodes. And like we mentioned, we're doing these remotely right now. So bear with us was we're trying to do the best we can with our audio and our setup here. Technically, uh, everyone is remote, not in our office, so hopefully everybody's staying safe and sane and everything else. And making sure that, uh, we're getting through this together. Um, one quick shout out for something that, uh, is being done by the marketing group at InterSystems that is coming up soon as these InterSystems Iris tech talks. Speaker 0 00:01:22 So I put the link to that. Uh, those tech talks in the description of this podcast, you can hop over and check that out. The reason I suggested is because the first tech talk is featuring three of our prior podcast guests, actually in Tom Dyer, Carmen Logue, and Benjamin Dubbo. So, uh, they're covering machine learning, AI analytics, some good topics there, uh, and these tech talks will be continuing throughout the spring. So go check those out. Definitely some, some good content that you can gather from some of our best experts here at InterSystems on those topics. So check out the tech talks and now moving into episode eight where we're featuring Russ. Um, Adam, give us a little bit of an introduction of who Russ is and why he's such an exciting podcast guest for our listeners to be excited about Speaker 1 00:02:02 Sure thing. We're really lucky to have him Russ practice internal medicine for 25 years before retiring to start a second career in clinical informatics. Russ has been at InterSystems for five years as a senior clinical advisor for interoperability. He's the standards guy here. He has been involved with standard Spellman at health level seven international, otherwise known as HL seven and a number of other organizations that work on developing interoperability in healthcare. Speaker 0 00:02:27 Nice. So obviously qualified. And I think as, uh, as you'll hear in our interview with him, I think that comes through quite clearly that he's very qualified to talk about this stuff. He really has a breadth of knowledge on all these topics. And so as a result, we really had so much in our interview with him that we are breaking it into two parts. So part one of this interview is in this episode in episode eight and, and what can people expect in part one and then kind of what is going to be broken into part two, uh, in, in a future episode. Speaker 1 00:02:53 So in part one, we'll go over some of the history of healthcare interoperability standards. We'll discuss some of the major innovations as well as how the demand for new standards has driven some of the technology technological advancements. In part two, we'll really focus on one of the new standards, which is fire fast healthcare, interoperability resources. And we'll go into a little bit more about how inter system supports fire and other standards. There you go. So exciting stuff. So without further ado, let's kick off part one with Russ left, which okay. Russ left, which thank you so much for joining us this morning on data points podcast. Can you please tell me a little bit about yourself and what you do at InterSystems? Speaker 2 00:03:37 So, um, five years ago I came to inner system as a senior clinical advisor for interoperability. I'm the standards guy I've been involved in, uh, the work that goes on and the standards development organization, health level seven, and until seven, as well as an under a number of other, uh, organizations that work on developing interoperability in health. Speaker 1 00:04:04 Nice talking about innovations in healthcare. Um, tell us a little bit about some of the innovations that you've seen over the years in healthcare and especially in electronic record keeping and how that has evolved. Speaker 2 00:04:15 Well, um, most people don't usually think of, uh, healthcare data standards as innovations, but in a very big sense they are, and it's started 40 years ago or a little more when hospitals started to have electronic systems more than one system in the hospital. Uh, but at that point, the, the use case was really just about connecting those systems. And that was a big challenge because it took engineers working for weeks just to connect to systems. Um, and as hospitals started to get more systems, a registration system, a laboratory system, uh, scheduling and in, uh, radiology and pharmacy system and so forth, they had to connect all the systems together. And that led to the formation of the standards organization. I mentioned health level seven, a, which is a healthcare data standards organization, and they were formed, uh, because there was a need for a way to address that use case of connecting, uh, systems within a hospital. Speaker 2 00:05:29 It was not at all about sharing data across organizations or, or with the government. It was just about connecting the systems in your hospital. Uh, so those first data standards, uh, one of the very first ones was HL seven, uh, version two, which people use just the HL seven at the shorthand for that data standard. It's still in use. It's the most used data standard in, in healthcare, but it doesn't meet, uh, interoperability needs that have evolved over the past 40 years, you know, and until, uh, 20 years ago, most of the data and healthcare was still in hospitals. Now in the past, when you years, we've seen this explosion of, uh, sources of data that are outside the hospital, everything from genomic sequencing lab to the internet of things, to a wearable devices, mobile devices. So there's an entirely new use case for healthcare data standards. Right. Speaker 1 00:06:38 Nice. So Russ, can you tell us a little about what all these standards cover? Uh, you've talked about sharing data within institutions and across institutions. Um, but what kind of data are we talking? Speaker 2 00:06:50 So we're talking about a lot of data and the types of data continues to expand in the 1980s. It was mostly about administrative data, not even what we would really call health care data was a patient's, um, identity and demographics where the patient was located in the hospital, what they were admitted for. And then starting to add to that their diagnosis, the reason they were in the hospital, uh, laboratory data was one of the first forms of electronic data in, in healthcare. But then we started to add other data and started to electronically document the care of patients, where the, the data included the history of the patient that was written down in a narrative. The problems diagnoses that the patient has, that had to be encoded in a way that, uh, machines could understand. And, uh, doctors and other clinicians in different hospitals, different places would know they were talking about the same thing. Speaker 2 00:08:03 And then there were that data that's electronic data like, uh, electrocardiograms, uh, monitoring data that is physiologic data in a sense, and comes in, uh, in a stream of electronic data. But you need a standard to capture that data and, and exchange it between the device that captures it and the, uh, uh, the electronic records system that needs to store it and record it so that the nature of data has continued to expand. The types of data have continued to expand. Now we've got genomic sequencing data that is something completely new in the past decade or so. So we have to continue to create new standards. Um, early on the standards were fairly simple, but they were in a sense complex to implement. And, uh, in the beginning, uh, it took, uh, experienced engineers weeks just to hookup two systems within a hospital. And when they change the software version of one of those systems, they had to do it over again. Speaker 2 00:09:22 Uh, that was increasingly time-consuming. As hospitals started to have more systems years ago, somebody observed that the average hospital in the U S has over 80 it systems within its walls. So interoperability sort of starts at home. You've got to connect those systems first, as you start to share data more broadly across organizations and across countries you need, uh, increasingly, uh, sophisticated let's say standards because everybody has to agree on this same, uh, concept of the data that's being exchanged. Uh, we call that, uh, agreement, uh, data model in the beginning. Um, there was no real data model except what was worked out between, uh, two teams of engineers working with different systems. Now we've reached the point that we have to, uh, create early elaborate, fully specified data models to describe not only the, the values of the, uh, of the measurements in the data, but the context of that data as well. And we do that with terminology, bindings value set binding. So a standard specifications for a data model includes, uh, the terminology, which is an entirely separate if you will set up standards, uh, that communicates what we mean when we say systolic blood pressure, for example. Speaker 1 00:11:01 So now we're talking about two different levels of data standards. The first is the data model or data structure, which allows machines or devices to reliably communicate with each other. The second is the terminology or context, which allows an end-user or machine to interpret the contents of data. For example, if an observation is made about a patient's blood pressure, a context code is paired with the data to say, what type of blood pressure it is or how the measurement was taken. Speaker 2 00:11:30 So those data models have become a requirement if you will, uh, for interoperability as, as the use case has matured to this idea of sharing data across organizations and data that is actually, uh, sits in many different systems and bringing that data together, right? Speaker 0 00:11:56 Yeah, it's really a complex problem. And I think a great explanation of all the different pieces involved in solving that. I think personally, you know, I have friends and colleagues that work in the medical space as doctors and nurses and I, and I've, I've kind of laughed at times when I've tried to have some of these discussions with them, and they oftentimes don't even know anything about these messaging standards that go on underneath it. And so it kind of speaks to the challenge of all these different systems and all these different, more and more modern applications and systems coming into the scene that you need to really be able to work between them. Um, you kind of talked about having sophisticated standards and being able to really encompass a lot of different things. And I want to transition that into one of the most popular, probably the leading standard that we talk about today, which is being discussed everywhere is fire, right? One of the most sophisticated standards that we have, um, can you introduce fire to our learners and kind of at a high level, explain what makes it such a powerful model to use, uh, when we're talking about solving this problem of healthcare interoperability. Speaker 2 00:12:52 So applier, which is an acronym for fast healthcare, interoperability resources and that's resources, as in a URL resource, in a computer sense, a fire was first proposed about 10 years ago, and it was proposed as a solution. What was increasingly being recognized as the interoperability need of the 21st century. This idea that we've just talked about, that that is in a lot of places and a lot of forms, and you need to bring it together. And fire was based on the way we do that in other businesses on the internet. Fire is in its essence, a restful API. It's a basic data model for healthcare data that you can use to build more complex models or particular use cases. And fire was meant to be a way to leverage the internet and web technology in healthcare. We had gone over the past 20 years or so from a time when all the data was in servers, in the basement of the hospital, offline to where much of the data is online, even in the cloud. Speaker 2 00:14:21 Uh, and as I've said in many different places, so we needed a standard, uh, that could connect all that data and could be, uh, implemented as a data model. And, and that's what fire is. The other necessity of that interoperability is that two different systems or rather all the systems that are sharing data need to be using the same data model. The challenge is that you could, for many concepts in healthcare, you could create more than one data model that reasonable to a human, uh, but if two different teams or organizations create two different data models for the same thing, there is no interoperability. So part of the idea of that, it's a technology that makes it easy to share those data model machine readable machine readable data model and implement them. Speaker 3 00:15:34 Thanks again, Speaker 0 00:15:34 Russ for joining us in light of Sanders Speaker 2 00:15:37 That make interoperability easy for a lot of developers, we sometimes take for granted how this stuff hasn't always been easy and how far we've come as an industry. Speaker 0 00:15:47 Yeah, absolutely. And I think hearing Rusk over some of the history of, of the healthcare and operability technology that exists and kind of how that's evolved over time, it's definitely interesting for a layman, certainly like someone who isn't involved in the nuts and bolts of the actual technical interoperability of healthcare, even for clinicians and doctors and nurses that don't get exposed to that underlying technology. I think just hearing someone with as much knowledge as Russ talk about that is quite exciting and quite interesting and thought provoking, I think, into how all of that works and how it's all evolved. Yep. Definitely. Each one of these systems required Speaker 2 00:16:21 Specialized knowledge, not just of general standards, but also how the standards are implemented in new situations, how the standards are changing with every update and all the layers of standards like Russ said from terminologies to the data structures. Speaker 0 00:16:35 Yeah. So it's a lot to unpack. And of course in episode, uh, sorry, in part two of this interview with Russ, we will be going over a lot more inner InterSystems specific discussion on how inner systems technology stack works with all this kind of healthcare inter-operability that we brought up and discussed in part one. So keep an eye out for that and it should be coming soon. So that's it for episode eight. Thank you guys for joining us and we'll see you next time on data points. Speaker 3 00:16:57 <inaudible>.