Making Rounds: Lab information, at your service [Podcast]

As with many other aspects of modern healthcare, laboratory information systems (LIS) have gone on a journey from basic pen-and-paper records to highly advanced digital systems. This digital evolution hasn’t just brought convenience; the applications to both individual and population health have grown exponentially with the availability of more data. Of course, with any digital solution comes the need to regularly update and optimize systems as time goes on. This added support is more than worth the cost; implementing and upgrading the LIS doesn’t just avoid negative redundancies, but brings with it precise insights and data-driven action steps the healthcare industry only could have dreamt of five years ago.

On today’s episode of In Network’s podcast feature Making Rounds, Nordic Head of Thought Leadership Jerome Pagani, PhD, sits down with three leaders at Nordic: Kevin Erdal, Andy Splitz, and Michelle Wiginton. They discuss LIS, how data coming from labs are different from other health related data types, and the similarities and differences between EHR and LIS upgrades. They also discuss the factors one should consider in implementing or upgrading LIS, utilizing it for personalizing individual care, and what the immediate future of lab technology means for clinical teams and workflows.

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Show Notes: 

[00:00] Intros

[01:50] How data coming in from labs are different from other health related data types

[06:11] The similarities and differences between LIS and EHR evolution

[07:48] Factors to consider in implementing or upgrading LIS

[20:44] Personalizing the individual care journey through LIS

[28:52] Immediate next steps for the LIS

[33:01] How these next steps will impact clinical teams and workflows



Dr. Jerome Pagani: Michelle, Andy, Kevin, thanks so much for joining us today.

Michelle Wiginton: Thank you.

Andy Splitz: Glad to be here.

Kevin Erdal: Happy to join.

Dr. Jerome Pagani: Andy, you have a considerable background in lab information systems or as we like to refer to it, LIS. How are data coming in from labs different from other health related data types?

Andy Splitz: Yeah. So, Jerome, it's been, as I look back, it's been almost 40 years that I've been involved in the LIS systems. Right from the beginning, as I got into LIS, they did have the beginning of those LIS systems working and they were quite rudimentary. And so, over the time it's been a big difference from what happened, where everything went. So, the data has changed from back then. It was a lot of data entry, a lot of handwritten results, even though the results were numeric most of the time we couldn't put them into a discrete field. And nowadays what we've been able to do is a very large number of them have been able to bend structured as discrete. So therefore, a lot of the gen lab numbers are all numerical. Some of the areas are a little different. You have blood bank, you have pathology. We have microbiology and most of them are all discrete, which allows us to do things with them. There are some areas where we still have to work very hard and that would be in the anatomic pathology area where there's a lot of text in those. But the rest of the remainder of the lab is really very helpful because it is in a discrete manner which we can use it in a lot of different ways.

Dr. Jerome Pagani: Let's take a step back for a second. Can you share a little bit more about the history and how LIS ended up as its own module?

Andy Splitz: Yeah, I mean, when we looked at back in 30, 40 years ago, a lot of instrumentation. So we had, we were one of the first healthcare areas, in the hospital, if you think back, that had a lot of instrumentation, even if it wasn't instrumentation, we had a lot of results. So with urinalysis and CBCs and other areas very results driven. So basically as instrumentation was developed in the laboratory, they then came forward and tried to figure out a way to easily move that data into some type of recording mechanism. So a place to put it, whether it be an electronic Excel spreadsheet or something of that nature. And so that's how really some of the first LIS systems were structured, very rudimentary, really trying to simply put in rows and rows of results that we could then utilize put into to save them into different areas and be able to get them into the patient's paper record. Again, as we started this, I started this 40 years ago, everything was handwritten, so we would handwrite a CBC result, put the piece of paper into the patient's chart. Luckily, as we continue to go forward, they were able to develop these LIS systems which really were able to connect to the instruments and allowed us to enter data. Now, again, it was discrete data, so it was easy. We could put it into spreadsheets, we could put it into different formats like the Excel format that we had. And so that allowed us to really develop over the years more and more instrumentation. Now, about 15 years ago, we had different systems. We started seeing multiple different systems coming to the market. So 15, 20 years ago we probably had five or six systems that were in the market, and that's where we really started seeing that data being helpful and useful. The EHRS weren't around when they first started and then as the EHR started being built around the systems, we were able to utilize it. If you think back to the Cerner system, the lab was the first system that they had. They then had pharmacy, then they had radiology, and then finally what they did is created an EHR, or back then it was called an automated medical record system. So back then the lab systems, the LIS systems really were trying to connect to something and there wasn't an EHR. It was really an HIS system. So that was what initially we started entering the results into from the LIS into the HIS. And then as time went by, we saw the EHRs coming into play. But it really was, the lab really was the first systems that were out there in the healthcare market just because of the type of the results we had and the ability for us to be adaptable.

Dr. Jerome Pagani: Kevin, Andy mentioned that the evolution of LIS systems really went from paper to these first versions that were largely just discrete fields, and that sounds a lot like what we saw from the platform EHR systems. Are there other similarity or differences there?

Kevin Erdal: Yeah, a lot of similarities. So you heard Andy talk about the capture of some of the data upfront, right. And using some of the instruments and eventually getting to that discrete mentality around the data. So that's ultimately what a lot of us have been working on for the last ten, 15 plus years at this point. From our standpoint, are we capturing the right information?

Kevin Erdal: And in the early days, right, it was a very lengthy note is what we saw come up, but it was an electronic note. So that was a step in the right direction. And now what we see a lot of is how do we design the system in a way that we have a contextual format which we can put that information in at the right time for the right care provider in a way that's a little bit easier to consume? Instead of having to read through two or three plus pages of notes. And now you hear people talking about the ambient listening aspect and we're capturing even more information, more data, which is fantastic. But yet again, we're kind of faced with that scenario in which we have to make sure we have a common place to put this in so that we can consume what the data is and then help make some decisions. So a lot of similarities. We're much better off where we are right now in 2013 than where we were in 2003, for example. But we still have a ways to go, quite frankly, in terms of are we putting that information that we're capturing or that data that we're capturing in the right place within the software itself? So a lot of good progress, a lot of similarities. I think we're all learning from one another, which is the beauty behind all of this.

Dr. Jerome Pagani: Michelle, where are we today with LIS? What are the big factors that are influencing the way health systems are thinking about the implementation or upgrade of their laboratory information systems?

Michelle Wiginton: I think today where most systems are looking at the specific LIS is looking at their workforce. As Andy mentioned, and what Kevin's been talking about, the evolution of LIS and the creation, how did it become a module in itself is unique. It was always driven by having a specimen needing to get a result timely to the clinician making life and death decisions based on that result. Stating to get to the right place at the right time. So I think that started as Andy articulated, that started the development of the labs. How do we get that captured into an electronic format that gets it to the provider faster for those quicker decisions to be made? I think as we go through, we're coming post-COVID and we're coming into ages of new regulatory requirements that are impacting lab in different ways than ever before. Regulatory requirements have always been there. We've been regulated in healthcare for many facets, but now it seems like there's so much more based on reimbursement and quality of care, and it's tying all of that behind those values that are coming in from an LIS and driving it, while you also have a very dwindling workforce in lab. COVID burned out a lot of not just laboratorians, but clinicians in general. So they're trying to find different jobs. And then you have a dwindling workforce that you're trying to meet the productivity demands while then you also hear from your IT folks that we have to take an upgrade. We need to change into a different platform because we have this regulatory requirement that we have to meet. So I think it's from a leadership standpoint, it's now trying to prioritize how do we handle this with an aging workforce, a dwindling workforce, and trying to accomplish all that set in front of me in a timely manner?

Andy Splitz: Yeah, I think just following up on that, Michelle. I mean, the other thing that's a little different in the lab, so Michelle really touched base on the regulatory side of the house and the things that are needed within the institution and the physicians, but also in the lab itself, there's a lot of new technology. So a lot of people would say, well, is it really you know, the lab has been had automation for years and years and years. Well, it's true, but now we're talking about because of the resource shortages, we're really looking at productivity needs. And so how do you have a lack of resources and still get specimens out? You don't have a choice. The timing needs to be there. The doctors need the results. You know, I want a stat within 60 minutes. So how do you do that? Well, that's where you come in with robotics. And so you're seeing a lot of robotic lines that are coming in and they can be small. They don't have to be the entire laboratory. They can be just a bench that can handle, you know, 100 different audibles, 100 different tests on that one instrument. And that can be automated. So there's a lot of things that are being done there, but that's where the technology, as the instruments continue to advance, we need the lab and the LIS system to continue to advance. And when productivity is required, then we really see that automation and automation is very difficult to try and tie into the systems. I mean, that's one of the areas where we've seen a lot of advancements in the LIS systems because those instruments need to talk to the LIS Not only do they need to understand the order that's coming in, they need to be able to read the tube, the expression on the tube. They need to match the order to the expression and they need to do the actual testing. Then they need to send the results back and make sure it's slotted into the right data spot within the LIS. So a lot of different parts there that need to be done and make sure that the systems all are speaking seamlessly to each other so that we don't have to have human intervention. So therefore we're looking at normal values that are auto verified in the ability to be able to use that new technology. So not only the technology in the LIS systems, but the technology and the scientific advances. You know, one of the areas we've actually had a whole new area modality within the LIS systems. We have molecular genetics now, when I was a lab tech, there was no such thing. And so now the great thing about scientific advances is we've had them catch up in the lab side and the labs side to put out these systems in these modules that are completely different. They're talking about thousands and thousands of data points within the system. And we talked a little bit about, you know, gen lab results and glucose and it's all being discrete. Well, all of a sudden, multiply that by 100 X and now you're talking about genomes and everything else. That data, not only do you have to be able to understand the data, you need to be able to record the data and then somebody needs to do something with the data and that's where you start getting into predictive analytics and the discussion of AI down the road. So it really is I mean, the base lab has become a commodity. Everybody can get a CBC and everybody can get a glucose. But when you start getting into the difficulty of right now, I need to reduce, I have a reduction in staff, I need to increase productivity or I have basically a new technology that I need to now interface with my EHR. So the doctor gets it in a real time basis and can utilize their scientific abilities and programing abilities to diagnose the patient in a timely manner. It really is a challenge that we're going to that we're continuing to face.

Michelle Wiginton: Andy really is a dinosaur. He's been talking about 40 years in the lab. Listen to all that dinosaur evolution at its best.

Andy Splitz: What can I say?

Dr. Jerome Pagani: We're going to come back to genetics and predictive analytics in a minute, but let's pull on that last thread dive a little bit more into tactically how health systems can be doing more with their incoming data and what that really looks like.

Michelle Wiginton: Andy spoke a little bit about it. I mean, right now, you know, 70% of all medical decisions made by healthcare providers are made off of a laboratory value. Those are statistics that are published in CDC, and we use them throughout medicine on the importance of lab. Not to undermine any other discipline at all. But when you have that many decisions being made on data, you have to get it to the right location at the right time and have it face up. So now with what we're talking about with EHRs and EMRs and everything feeding into it, how do you make sure that the clinician is getting the right information at the right time? And it's not very now and a ton of data that are data points that are feeding in and you really have to challenge to how do you provide that face up? So now we're getting into a lot of the predictive analytics, a lot of things into data warehousing and data mining where you can have syndromic surveillance. You can have, you know, most locations now, especially if they have a good data warehouse, they can predict that you have a flu outbreak, we'll just say flu, outbreak in a specific geographical region because of the tests that are coming into that laboratory, they're testing positive for flu A, flu B, it's in this general population. They can mine that data and they can use that data to already alert the health departments: “we're already seeing a flu outbreak early in the season in this part of the city. We already are X percentage positive.” And I say flu just because, you know, that's just an easy one that everyone can relate to. But that's the way you can start to take the data that is very discrete, easy data that people would say: “Now we just put it on an instrument, we get the result.” But you can start taking that data and you can start transforming lives and population health based on that data and doing those predictive analytics as we move forward into mining the data, that that large pool of data, and getting those points to the right people.

Kevin Erdal: And you hit on it, Michelle, right. We have years of data around this very topic so we can alert to say this is something we need to look further into and we can start to predict some of these outcomes because we've been storing this information so long. So I think those are some of the huge values that we need to continue to lean into.

Michelle Wiginton: Yeah, I think one of the challenges there, and Andy can, you know, he can speak a little bit to this as well, but, you know, getting that standardization of data, that's going to be the key. We talked about it with HL7 for interfacing, you know, starting to develop those key standards that supposedly all LIS is going to talk. We're going to talk the same talks that we have LOINC codes, we have SNOMED codes. So we're trying to start talking language that every test can truly be analyzed as a 1 to 1 instead of having 1 to many. Because as you get into your predictive analytics and your data mining, it, everything can't have a different definition. We need to standardize what we're calling the test so that we can make actionable decisions off of the data that's coming in. So I think that's one of the things that is still, it's still in the works. And I would say I'll defer to Andy to speak on our history, but I think we've came a long ways in standardization, but I think we still have a lot further to go so that we can truly get the best benefit from the data that we're mining.

Andy Splitz: Yeah, I think that, and that's a very important point because that's where we need to go. As Michelle mentioned, SNOMED, LOINC, those are some major things that the industry needs to figure out, but it needs to happen across everything, right? Not just laboratory. So that's where we get into the EHR, we get into data normalization. That's across all of the patient medical record. That's going to be the important piece. That's the part. Right now, we can't figure out what we call each other. So am I a number? Am I a retinal scan? Am I a fingerprint? If I don't know who I am, I can't tag the data to that person. And so that's one of the biggest things we have is that medical record or the identifier that we have. So right now is great as our LIS systems and our EHR systems are, that data stays within that record. It never gets to travel to a universal, let's say, opportunity or a national opportunity. We are all still within our regions and unfortunately, because the data isn't normalized where it needs to be, we can't take that data and move it over, let's say across the country or even down the street to a different EHR vendor and have the same necessarily the same idea or the same clinical outcome. The good part about the lab is we have normalized a lot of the data within the lab. And so those LOINC codes and SNOMED codes really have been able to help us out in those areas. They're not fully adapted everywhere to the level they should be, but at least we are probably five steps in, as Michelle said, going from the dinosaur of handwritten pieces of paper over to where we are now. We really are much further ahead. But we need to make sure that everybody across the healthcare continuum at that level.

Kevin Erdal: Yeah, I read a book written by a couple of docs on this topic, Designing for Health: The Human-Centered Approach, and it almost sounds like if we don't apply these standards, we really don't have anything at all. Standards without application is just some really good data sitting in a warehouse somewhere.

Michelle Wiginton: Wow, Kevin, that sounds like an interesting book. What was that book that you read?

Kevin Erdal: I’ll let Dr. Pagani hit on that.

Dr. Jerome Pagani: That is a great plug. And Kevin, you bring up a really interesting point about the need for intentional design. And I think that's one of the key factors that's missing in the advancement of care and making it really work for those on both sides of the care delivery, those giving and receiving.

Kevin Erdal: Yeah, absolutely. And we've hit on some of this throughout the dialog, which is usually the case, right? Andy hit on the exchange of data or some of the interoperability components. We're really good at that. We have a lot of technology within the healthcare ecosystem to do some of these things. But again, if it's not applied in a contextual fashion, then we're really not advancing the decision making. We're really not helping advance care. So those are some of our core next steps. But to everybody's credit, right, we've been able to bring technology to some of these problems and we've seen some great advancement. Now we just have to do it at scale. Agnostic of what the technology is or what system the patient is presenting themselves to, to make sure that all systems are talking together with a common goal, to be able to advance that patient care, hopefully in an expedited fashion.

Dr. Jerome Pagani: So I think that leads us perfectly into the next question, which is really we talked a little bit about the advancements in population health, but what most patients are really interested in is how does that translate down to me you know, when is what is my end of one treatment plan look like? So let's take a step back maybe, and ask what is the integration of genetic information and other things that a modern LIS system can do to help personalize that care journey for a patient?

Andy Splitz: Yeah, I mean, I think one of the things there and actually just to go through a little story there, a little event that that we talk about in the in the laboratory that I thought was quite ironic are really very, very exciting. I know I'm a lab tech, so I'm very, very excited about lab things. But literally recently they have been able to track COVID, different COVID strains through the sewer systems and being able to test that. I know it's kind of gross, but try and test that literally and see that different strains are coming out, where the strains are coming out where in different populations, in different cities where different strains are. So therefore they can get treated and they can get vaccinations needed, that literally is laboratory. I mean, that is taking that testing the data and then bringing that to the entire population, not only in a in a local region, a city town within streets even, but across the nation. So it really can help dictate the science. So we're taking a lab test and that results of those lab tests are helping us determine the strains that we have for COVID, which then is determining the vaccinations we're putting out. So it's a nice little step up of all the way through from a med tech or a test that we're doing in the laboratory, all the way to direct patient care in a year and again, against population. So as you just mentioned, you talk about personal care, but it did both personal care because we're modifying the vaccinations that we're giving to the people, but also population care because we now know that this population may have 20% of the entire population is now got the virus. So it really touches a little bit on both. There's one other area that is out there, and it's really, it is called Lab 2.0. It is a movement, I guess, or a group of people that are trying to utilize lab data to bring it to not only develop studies through the use of a large amount of data, but then bring it to the ability to bring it to the patient level. So to be able to do predictive analytics because of the results that are coming through with a patient at a certain age or a certain genomic background, they then can see that opportunities where different treatments will be able to be more effective or less effective as going through. So those are, you know, one is an example of how we're using lab as an everyday method. And no one really understands it and the other one is, is really a group of within the laboratory that really is trying to utilize the results to go to the next level, to get it to become personalized data.

Kevin Erdal: That precision medicine based approach is, we're seeing a little bit more success as Andy is hitting on right now as an end of one with some accurate lab data and some accurate information for my ear, great, the clinician can help translate it and hopefully diagnose or prescribe medication, whatever it may be to that individual. But now if we expand that to tens of thousands and hundreds of thousands and millions of people, we can be much more accurate and more efficient in the care that's being provided. And that's why we have to be able to scale and capture all this information so that we can not only support the large population, but like Andy said, we're also supporting that individual in a very precise fashion.

Michelle Wiginton: You know, genomics is such a touchy subject right now in, I think, healthcare today, because when you take it down to the patient, it's that how informed does the patient really want to be? And while science we are all scientists and we love to see all of the markers and the sequencing we can do and in getting the mappings and then the research that surrounds it to uncover that this pool of patients that carry these genes and markers over time developed X disease. And I think genetic counseling, that's why there's so much involved with it today, is trying to engage the patient on how much information do they want to know based on the data that we now can provide them from predictive analytics and mining of the data of all of the points that we're taking and have to be able to do the research behind it. But how do we take that to the patient? How do we do this in an informed manner that you hear patients a lot of times say, I don't even know if I want to know, like, is it going to change my life? Can you cure me if I know that? Is there a cure? No. Today there's there may not be a cure, but it is helping to potentially develop a cure. And I think I think that data is so powerful. But at the same time, we have to engage with our clinicians and our patients to figure out how do we educate our patients on how powerful this data is and how it can be used. I think even taking it down to how many people want to opt into the health information exchange in their community, because if it's not, if it's not communicated appropriately, all of a sudden, they feel that their private data is being shared with people they did not give access to have that data. And now they have all of this information about me. And where the whole goal of an HIE, and this is totally off topic but similar from a genomics perspective, I think, it's a way to give better care to the right clinicians when a patient may show up that they're able to get that information, know they have a critical illness or know something that's going on with that patient to have better care. But if it's not being communicated appropriately and the right education getting out to those patients, then it's not getting the best to the individual of the outcome that we're trying to achieve.

Kevin Erdal: Yeah, the patient communication is always critical. And as a data geek here, I'm sitting here thinking, I want all the data all the time, right? Yeah. I am also thinking about five years from now, right as we're capturing this information in a discreet fashion, while we maybe don't have a care in today's world as we sit here in 2023, we don't know exactly what clinical research is going to lead us to and guide us to in 2030. But if we don't capture the data now, we might look back and say, goodness, if I just had this discrete information, we could really start to make some impactful change. So I think at least capturing it right and storing it in a safe and secure fashion is a step in the right direction.

Michelle Wiginton: Absolutely.

Andy Splitz: Yeah. I think it's funny with genomics and the genetics piece of it, it really is. Like you said, Kevin, if you have it all mapped out now, it's not going to change. I mean, that's one thing. It's not like you're going to grow and it's going to change. It's going to be the way it is. And so in theory, I know with the 23andMes and all the other companies that are there, you literally can resubmit. They will allow you to resubmit as the years go by and see if anything that they had. So if we know more about the genome and the genetic makeup, we can you can resubmit your pattern and then they'll come back with you on a new read. And so I think this is very exciting down the road. But I also agree with Michelle if they can't cure it, I don't want to know.

Michelle Wiginton: I knew you were one of those patients. See, that's what I'm saying.

Andy Splitz: Definitely one of those patients.

Michelle Wiginton: Yeah, yeah.

Kevin Erdal: Too funny. Well at least we have one accountability, right? We can make sure that technology or infrastructure or anything of that nature isn't standing in the way. The proper use of data is something that we all have to contribute towards, no doubt. But let's not let technology be the reason why we can't at least advance.

Andy Splitz: Exactly.

Michelle Wiginton: Agreed.

Dr. Jerome Pagani: So there's a lot that's exciting about this future that we've been talking about, and health systems are eager to get there. What should their immediate next steps be with their LIS to help get them to that future state?

Andy Splitz: So, Jerome, the, what we're seeing with the LIS transformations nowadays really is where we talked about them being really a standalone system, and that's the way they were for years as they were developed, they were standalone LISes and then they slowly started being moved into other systems or interfacing with other systems. What we're seeing over the last four or five years is really we're seeing the change. So for a while it was go down the road of if you have ancillary systems, you want the best in breed. So it was a best of breed thing. Well, interface, they don't interface very well, but they are able to transmit data back and forth to each other because the fully integrated systems were very mediocre and all the ancillary systems. So the ancillary departments did not want to give up the functionality they had to go to an integrated system. What we've now been seeing over the last four or five years is that has completely changed. So now we're seeing that the integrated systems, the Oracle Health, the Epic and the Meditech, those types of systems really have been able to expand their laboratory systems in their ancillary systems to be able to be fully functional. So about five years ago, ten years ago, I created a document called LIS FAT Toolkit, as well as 350 items, functions that were utilized in what we would call a best of breed LIS system. It was then taken by the all the LISes and making sure that they all had these type of functionalities within them. And what it really did is it allowed the integrated systems to come up to speed. So now we have the three majors with the EHR really fully integrated. And so it really helps a lot because one, there’s not interfaces, there's not an issue between the systems. They're all integrated, so there's no interfaces back and forth. What that also allows is rules within the EHR to be triggered directly through the lab. So as you get a lab result, let's say you get a resistant bacteria when you're doing an antibiotic anti bio gram, you can now have that transmit over to the pharmacy and the pharmacist can see it immediately and then make sure that the medication that the patient is on is appropriate or not appropriate and make a change. That was very difficult to do when we were in a best in breed system. It was interface. We couldn't get that type of true integration. And that's what we're seeing now as we move forward. So it's very exciting time for the LIS systems. They're fully functional within the integrated system. It is it is causing some issues with the ones that were best in breed. And now really, in all honesty, all the top five or six are pretty much all seeing those functionalities with the best in breed.

Kevin Erdal: And just because we're moving to some of those integrated modules or systems, right, we don't want to get rid of to that legacy data. Like Andy mentioned. And Michelle a little bit ago, we've been doing this for a while, so we have numeric and relevant data for the last several years. So now we need to make sure that we're governing that data and we're making sure that we're hanging on to it in a safe and secure fashion. But we're also starting to wrap some of the identity and access management controls around it to make sure that when it is time, whenever that is, to release that information or some of that data, that is for the right patient in the right context. And so I kind of hit on this a minute ago as well. We've got to make sure that technology isn’t our limiting factor. So making sure that we have scalable platforms that can grow over time because we're only capturing more data, right? We're only capturing, we're only going to have additional instruments and devices captured more and more data over time, which is very exciting. And I think it's going to make us more effective. But here again, we have to make sure that we can scale. And it's not just a 1, 2, 3, X kind of mentality anymore. It's going to be a lot more than that as we continue to evolve across the health information system landscape. So the governance of it, identity and access management, scaling, infrastructure, all key components to really support the evolution that we're experiencing right now.

Dr. Jerome Pagani: And how will this change capabilities for clinical care teams and impact their workflows?

Michelle Wiginton: I think as sites are looking at this LIS transformation, looking at data warehousing and how they govern all of the data that's coming in, it definitely expands outside of the LIS. It is a considerable amount of data, but by it being integrated and used from a clinicians workflow perspective, it is truly putting the right information in the clinician's hands at the right time. So if it's your loved one that that physician is taking care of, you want the most timely care for your specific loved one. You want it to be timely, you want it to be right and you want it to be directed so that that the outcome can be positive. Taking all of this data, putting it in front of that clinician, is going to do exactly like what Andy articulated. We've got rules that are going in front of clinicians that are telling them, you've got a duplicate, you've got an alert value, you've got it, go out and look at this additional result that's also been provided. It is important for the care of this patient. You may be on sepsis protocol. There's lots of integration with sepsis protocol, but this is truly touching every aspect of a provider and where they may be touching that patient and the lifecycle of care from physical therapy to radiology to pharmacy to nursing, it's allowing that collaborative interoperability between every discipline that is coming in contact with that patient.

Dr. Jerome Pagani: Kevin, Michelle, Andy, thanks so much for joining us today.

Michelle Wiginton: Thank you.

Kevin Erdal: Always a pleasure.

Andy Splitz: Great to be here.

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