Making Rounds: Under new (data) management [Podcast]

Whether an organization is updating their current electronic health record (EHR) or moving from paper records for the first time, diligent data management is paramount, especially given just how many data points that organization will now soon have. Data for data’s sake won’t cut it, though. Unless their data infrastructure has been designed for actual use by and for actual clinicians, all the digitization in the world won’t make the system suddenly helpful. Rather, careful consideration needs to be given to who the data are for, how they will be used, and if it ultimately is helping or hurting the clinician burnout crisis.

On today’s episode of Making Rounds, Nordic Head of Thought Leadership Jerome Pagani, PhD, sits down with Digital Health Practice Leader Kevin Erdal and Head of EHR Implementation Services Andy Splitz. They discuss the hard work of EHR implementation, the global landscape where organizations are only starting to move to digitization, and how imperative it is that data management is done well and with real end-users in mind.

Listen here:



In Network's Designing for Health podcast feature is available on all major podcasting platforms, including Apple PodcastsAmazon MusicGoogleiHeartPandoraSpotify, and more. Search for 'In Network' and subscribe for updates on future episodes. Like what you hear? Make sure to leave a 5-star rating and write a review to help others find the podcast.

Show Notes:

[00:00] Intros

[01:39] EHR implementation and data management

[08:10] EHR implementation globally versus in the U.S.

[15:43] The vision for how data will be used both inside and outside the EHR

[24:02] Managing data infrastructure and BI modernization in tandem

[29:02] Designing data management systems with the end user in mind



Dr. Jerome Pagani: Andy, Kevin, thanks so much for joining me on the podcast today.

Kevin Erdal: Happy to be here.

Andy Splitz: Glad to be here myself. Thank you.

Dr. Jerome Pagani: We're going to talk a little bit today about how the data and technology landscape is really shifting. And we're starting to incorporate new data types, things like consumer generated data from wearables, Internet of Medical Things. We're seeing different types of data, continuous data monitoring, things like that. So as health organizations are considering how to mold their infrastructure to accommodate not only the sort of regular clinical data that you would ordinarily find in the EHR, but incorporate some of the things from these new data sources, what should they be mindful of? What should be top of mind for them, Kevin?

Kevin Erdal: Yeah. So one of the biggest things is how are we going to get that data into a relevant place? Right? So if we're going to capture more data, which we've gotten really, really good at, like you mentioned with IoMT, we're seeing a lot more with hospital at home. It's great to be able to take your blood pressure at home. How are we going to get that into the EMR in a clinically relevant context for a clinician to be able to help make a decision or to help monitor that particular patient from afar? So what infrastructure do we need to be able to support that? Start to answer some of the questions, do we want to store some of that data either inside and/or outside of the EMR is becoming critically important, so that it plays into not just your infrastructure strategy to help support the overall interoperability, but then also your data strategy. Right? This is very relevant data. It's starting to help a lot of us understand what is happening with a patient to a patient outside of the four walls. As we've talked about now for the last 3 to 5 years now, we need to help make some decisions based off of that information that is starting to flow. So it really goes back to some of the basics behind transforming data into information and make it frictionless in terms of how we're consuming that data into the clinically relevant context.

Dr. Jerome Pagani: Andy, the EHR landscape is changing really all over the world. In some places, what we're seeing are implementations for the first time and places like the U.S. where we've had existing EHRs for a while. We're either seeing switch overs or consolidation of systems. How are these factors and that new data landscape really impacting implementations around the world?

Andy Splitz: Well, I think implementations around the world is really two different things. As you mentioned, the U.S. is, you know, completely saturated. The EHRs have been around for a long, long time. We're on the fifth or sixth version of those generations of those. And so it's really different globally what we're seeing is they're just, the healthcare is the way we think it is. In the Middle East, they're trying to get it to where we are. So they're probably 15 years behind where we are right now and we're seeing the systems go in. But the funny part is obviously we're putting in current version systems in the U.S. So what happens is they went from paper to all of a sudden, you know, the fifth generation of the Oracle Healths or the Epics, and it's a massive jump. And so you've got physicians and clinicians all of a sudden getting all this technology without really understanding how they got to that point. What do they need to do? Do they have the structure in place? Do they have the data in place, or how do they get the data? They're coming from paper or they're coming from a first generation EHR. It's totally different at this point. So what we're seeing and especially globally, is that this is a big jump. They need help there. A lot of them are not very successful initially. They will use the minimum required of an EHR to get through. They may even use some Excel on the side of these. So that's one thing. And again, they're not able to utilize the data where they need to go. There is no real BI on these areas because they're not seeing that, they can't get that data into the system. Even if they can, they're not quite sure what to do with it if they get it out of the system. In the U.S., as Kevin said, I think the wearables and those type of things is different. And that's where the EHRs still aren't quite sure what to do. So you got two issues there. Kevin went down the technology side. I'll go down the clinical side. Do the doctors want your blood pressure in there? I take my blood pressure every morning. I sit there, wear wearable, you know, Apple Watches and we sit there and have my O2s and everything else there. That's a huge amount of data. Is it going to actually clog the basically the EHR? Right now, the EHRs are built just basically, as, you know, electronic systems that holds paper and a ton of data fields, thousands of data fields. And what are we going to do all of a sudden? We're going to be putting in probably exponentially four or five or six times what's in there already? How is a doctor going to know what’s a lab result and how is the doctor going to know the difference if that lab result came from a glucose monitor from your house or all of a sudden came from a lab core? The lab core is a trusted, you know, resource and a trusted vendor with the kit that you did from your house or a device that you had or watch that you had, who knows if that data is accurate or real. There's been no QC done on this. And so that's where I think we have to be careful. Yes, it sounds like we want to get a lot of this data, but on the other hand, I'm not quite sure the physicians can handle it. And again, do they really want it in there? They would like to make sure their patient record is as clean as possible with the data they need to diagnose. Adding additional data elements doesn't mean that helps.

Dr. Jerome Pagani: We've talked about this before in other contexts, but it sounds like you almost need a data label that pops up in the EHR so physician knows where the data is and how trustworthy.

Andy Splitz: Correct. It could be a separate module or it could be, you know, in Oracle Health or in Epic, it could be a whole different, you know, click on this key and you get all this data that is patient driven data, keep it out of it, out of the predictive analytics or any of the analytics you're doing, because again, you don't want to sit there and have unsubstantiated results in your record or comments in your record or something that's in your record that a physician hasn't reviewed.

Kevin Erdal: Yeah, we've been really good at throwing technology at perceived problems for a while. Right? But you hit on it, Andy, trusted and relevant data. I mean, that is really what it comes down to, is it helping the clinician care for the patient? Is it helping a patient care for themselves? If those answers are no, then what are we doing? Then don't spend more technology or spend more time on the technology.

Andy Splitz: Yeah. I mean, I literally, in my own life, I went through and had high blood pressure incident and basically one doctor said, Please take your blood pressure every morning. I went to another doctor. He said, Throw away your blood pressure cuff. I don't care what you get every morning. You're not trained in blood pressure. So there you go. There's two different opinions. I got both. So, you know, I hate to say it. I think he's correct. If I can do it, okay, I am a med tech, so I could do it in the military. Most people aren't. And so therefore, whatever they get could be completely erroneous and therefore something that they don't want in the record.

Dr. Jerome Pagani: Andy I want to go back to something you said earlier, which was about implementations that are happening in other parts of the world where they're going from basically paper and pencil, right, to sort of very mature, you know, fifth or sixth iteration level EHRs. How is that impacting clinical workflows, and what do organizations need to be thinking about as they're setting up workflows with their clinical care teams?

Andy Splitz: Yeah, I think I think you've got to, I don't want to say dumb down because that's a bad word, but I think you really do need to streamline or make sure your clinical workflows. The question is, does the site, you know, the hospital you're going into. We had care plans years ago, you know, and so you had a paper care plan or you had a process that you use to have, came in with a broken leg. These are the tests you ordered. This is the X-ray audit. These are, the majority of that was all in paper. If you have, if you're at least to that level of care, then go to electronic. Theoretically, you'd be basically moving those care plans into an electronic system. Little easier to do. Now, the question is, do they have the knowledge to do that or is that something that you would, you know, bring in a third party? But those are the type of things that you need to really follow. Otherwise all you're doing is getting a bunch of data. So now you're going from paper where you know exactly where the checkmarks are and where the data is, to a system that you're searching and you're hunting and pecking. And that's where you start seeing physician burnout, that you start seeing, you know, clinician frustrations because they're not able to take care of the patients. Instead of being 10 minutes with the patients they’re now 20 minutes. And the reason they're 20 minutes is because they're hunting and pecking for the data they need to be able to diagnose. So I think it's a careful slope. One of the things that we're doing as we're trying to implement over in the Middle East, is we are looking at that. It's a military institution and we are very worried about the fact that we're going to bring them some of the advanced technology that the U.S. has way before they can actually utilize it and manage it for care. And so that doesn't help. We need to make sure we look at that process.

Kevin Erdal: Yeah, I was just joking with one of our non-U.S. clients a couple of weeks ago where you can't just jam the piece of paper into the computer screen and expect it to work. Right? You got to think through what is the workflow, what data are you capturing, and then how are you going to use it right before you can start to get into even some of the downstream conversations?

Andy Splitz: Yeah, I mean, if it discrete data, everyone wants discrete data. On the other hand, paper is usually nondiscrete data, right? It's a sentence. So they wrote something down, that's a problem. And so you're going from that paper system that's nondiscrete data and you're moving into a system that must be discrete data, or else you can't utilize it. So it is a change, a big change.

Kevin Erdal: As a data geek, though, discrete data is very nice when you have it.

Dr. Jerome Pagani: So before there were the big vendors in the U.S., there were systems that existed that really focused on sort of narrow parts of particular care delivery, right? So you had, you could have a system that was specialized for one specialty or something like that.

Andy Splitz: LIS systems.

Dr. Jerome Pagani: Yeah, LIS systems. So I'm sure there's still some of that in the U.S. We were talking about trying to integrate the EHR with other systems that are being used for sort of very specific purposes. But I imagine globally that same thing is happening that happened in the U.S. 15 years ago.

Andy Splitz: Yeah, I think there definitely is. I think especially with the lab information systems that have been around for 30 years. Over 30 years. They're very mature. But the movement for all these health centers or these institutions are really to go to all encompassing so that it's a integrated EHR health system that includes those systems. And so what we've seen in the United States over the last ten years is those going away. And so you moving to, I hate to say it, an Oracle Health or an Epic because those are the two big ones, and you're moving away from some of the smaller systems. So you're moving away from your lab systems, you're moving away from your packed systems that were all individually, best of breed is what we used to call them. And now you're moving to integrated. So I see internationally, same thing. Lab was saturated pretty much because you can't run lab instruments without a backbone and they were the backbone. So you have a lot of really, you know, current first generation instruments in the laboratory. That is one thing that the all around the world that they have. That is something that's been really up to date, but they need the systems to manage them. So they've managed them, like you said, in department by department radiology in the pack system, you'll see that where you go, they're pretty modern losses. You'll see them when you go. But what you don't see is that EHR and so now this is what we're looking at. The United States has pretty much been doing it. We're on the back end of it now. We're going to see it globally.

Kevin Erdal: Yeah, and I think one of the things we're seeing stateside right as we're starting to go with an integrated environment now we have these legacy systems. So want to make sure as we sunset, we're actually doing the rationalization. We're archiving the data the right way and then leveraging our long term and hopefully we can learn something there at the global level so that we don't have to go through some of the heartburn, if you will, that we're having to go through right now stateside with some of these legacy systems itself.

Dr. Jerome Pagani: So there is a lot of legwork there to kind of go through that process with the legacy systems. And I hear you saying there may be some efficiencies gained.

Andy Splitz: Oh, definitely. I think there's efficiencies gained going to an integrated system. The arguments always been I've lost some functionality. Again, I don't see that. I mean, we're talking about maybe a 2 or 3% loss of functionality. Sometimes you're actually gaining functionality because you're sitting on these older systems, these legacy systems that are ten years old, and the new functionality is better than the old functionality. And so it seems like you're losing it or changing, but the data that you're collecting is much cleaner and you're actually, the process is much better and the productivity is much higher. So these integrated systems are really the way I think that, you know, we're definitely going that way in the United States. And I expect that to be where everyone goes internationally.

Kevin Erdal: And as those integrated systems are transpiring and evolving over time, we're starting to see some of the data become more available as well. So as Andy mentioned a little bit ago, sometimes it's really hard to get information out. Well, when we go with an integrated environment, the data is more readily available. You don't have to do as much integration and normalization, which can be a plus side on the clinical insights, if you will, from the data that we're trying to capture throughout the journey.

Andy Splitz: Yeah. And I think one of the things that we're, as we're talking through the data, looking at the data, we're getting much better in the United States because we are going to these integrated systems. One of the big problems that we had, I think up until now where we really haven't seen the benefit of this data, you know, data migrations or the business integrations that we're able to do. We're really not able to do that because the data has been coming from multiple different legacy systems. So you've got a, you know, data set from the lab that's coming and the data set from the park system is different. The basis that from the pharmacy is different and normalizing that data has been a nightmare. And we're getting to the point now that we're finally moving to integrated systems where the data is now in structured data within that system and you don't have it. That's going to be a big problem internationally. But I think right now in the United States, what we're seeing is we are finally getting to the point where that data is something that we can use. We can, you know, basically take in that data and then move it out into different areas that we want to. And that's just because it's not that the technology hasn't been there over the last five years. It's been the data hasn't been in the format we could use.

Kevin Erdal: Yeah, we just elevated our expectations, now we expect any note, anything to be in a relational structure to drive insights from. So now we're kind of out the other side to say, well, hey, it's easy to be able to get this discrete data and give you some insights. Now why can't you do the same thing for these notes I'm capturing? Well, that's unstructured, a little bit more difficult, but there's technology we can apply to that and make it a little bit better.

Dr. Jerome Pagani: Andy, Kevin, what's the vision for how data will be used both inside and outside the EHR? So really, how is a modernized API infrastructure going to support the vision for what we can do in clinical care?

Kevin Erdal: Yeah. So we want to drive those insights, right? We want to learn from the data that we've been capturing. How can we be more efficient, more effective? I referenced the transformation of data into information a little bit ago. A big component of that in today's world is the EMR. The EHR specifically stateside. That's a huge chunk of data, 60, 70, 80% depending on the health system that we're talking about. But we also have to be able to integrate some of those ancillary systems. In some cases, we're talking about health equity, we're talking about SDH data. We talked a little bit already today about IoMT data, so we're flooding ourselves, in some cases intentionally, with even more opportunities to learn something about the populations of patients that we're caring for. And or to get more precise, around some of the specific comorbidities that we're trying to care for as well. So there's now more data coming at us. Now we need to make sure that we have infrastructure around or in some cases adjacent to the EMR itself to modernize the way in which we're capturing data, the way in which we’re storing data, the way in which we're integrating data like Andy said, at least we have some of this discrete available, more readily available to us both within the EMR and outside the EMR. Now we need to start to integrate and we can start to put modern BI tools on top to make action oriented decisions. And I really want to emphasize action because that's the state in which we're at least stateside here to be able to say, okay, we have information, how what do we do to make a difference? It's one thing to see conditional formatting on a pretty dashboard. Red, yellow, green. So what? What are we doing? Does it really matter? It's red. What do we do with a red metric? Nothing, maybe. Or do we say, Hey, let's activate our care team and reach out to this patient population via SMS text and try to make sure that they're coming in for their annual wellness check. That might be a nice use case for some folks to consider to be able to say not only do I have the data, I have the business intelligence on top of it, but I'm also working with my care team or with my clinical team to associate the action to that data.

Andy Splitz: Yeah, I think that's an exciting thing that's coming up now that we're starting to see, because for the last two or three years, all we heard was population health, population health. Population health is great. I mean, you understand what's going on in your in your community, in your neighborhood. But population health doesn't help John Smith. Doesn't help me. Doesn't help them, we need personalized data. And so Kevin was mentioning as we're finally getting to the point where we're able to move that population health data, which is great, but move it to personalized data. So now we take all these data elements, we're able to sit there and analyze those data elements, whether it is in the EHR, which that's not necessarily the role I think the EHR is playing. I think that as a data repository for all these, that's where the clinicians work. But I truly believe, like Kevin said, an ancillary system to that that is designed to take that data and truly be able to convert that into actionable items for the physicians. Now, you got to get it back into the EHR because that's where the physicians work. So that's a secondary issue that we have to deal with. How do you get it back in so you can consume the data in that other system? But we need to figure out how do we make it actionable to the physician, make it actionable for the physician all the way down to the care team? And that's the exciting part, is I think we're getting very, very close to that point. We're still having issues with integrations. EHRs like to be the sole source of everything and that can be a problem. And because, again, they can't move fast enough, dictated by regulation, not dictated by the new technology. And that's where those ancillary systems can really come out.

Kevin Erdal: And we're even putting some action into the patient's hands, too, right? I mean, you're talking a lot about the physician, which is 100% accurate. But as an individual, I want to be responsive all for my own health, but I need the expertise from my care provider. Right? So as these insights are derived now, we have different mechanisms to interact with the patients to push some of that information, to say, Hey, Kevin, consider something other than that Snickers bar. Go for a three mile jog instead. Right? And what is that going to do to my health? Why do I care? Well, based off of other people like you, if you start to make some of these lifestyle changes, here's how your health will progress and you might come into the clinic less so, you might have less downstream impacts. So I think some of that is very exciting to be able to see patients taking advantage of the expertise from their clinician and really taking their health into their own hands, which we should expect to see more of.

Andy Splitz: Yeah, definitely. If the patients have to pay for the healthcare, we should be doing it more.

Kevin Erdal: I know it helps me.

Dr. Jerome Pagani: To bring up two of people's favorite topics. It sounds like I'm hearing a role for automation and getting the data in and out of the EHR, for sure, as well as generative AI for on the engagement piece, both for the clinicians and for the patient side.

Kevin Erdal: Yeah. Let's get that clinical knowledge in context baked into our workflows and our processes as a patient. That's what I want, right? I don't just want a bot to tell me what to do. I want to know that it's augmented with clinical relevance and expertise and that's what we're really seeing, start to move the needle within some of our more innovative clients, be able to say, Hey, we're driving some unique insights. We know how to reach Kevin Erdal as a patient, which is very exciting. But now to put it in context and I'm going to start to make a difference in his day to day life by introducing that clinical expertise with the bot itself.

Andy Splitz: Yeah, it is very exciting area and a very exciting time to be able to do that type of stuff and bring it to the personalized data. But the tough part is, as Kevin mentioned, very specifically, we’re on certain cutting edge clients. And so I think that's one thing we've got to realize is most of us don't go to cutting edge physicians. We go to the physician down the street. And so I definitely believe and understand that. Yes. So getting, AI is a tough stretch for me. Predictive analytics is easier for me to understand. This happens, this happens, this happens. This is the way that you're probably going to go. I think we will get AI, but we need millions of data points to do that. We are obviously talking today about how many data points are there. We just got to make sure we can consume them. But, you know, the key piece here is how do we affect healthcare across the country? Not in the medical hubs of the, you know, in the colleges and yes, in those medical academies and yes, in Boston and New York and some of the bigger hubs. Great. They're doing some amazing cutting edge things. But what is the rural person doing in the middle? How do we get that technology and that knowledge all the way to the patient, not to the patient in New York or Boston or California, you know, L.A., but to the patient in Missouri or somewhere else in that are, that's I think the part that I'm trying to figure out is, yeah, we've got to take step one. Step one is definitely always going to happen at the institutions, you know, the medical institutions, and that because that's who has the money and that's where the experiments are going and where the technology is going. But the other thing I think we need to really walk down the road is how do we get something to everyone? And I'm not quite sure we're there yet. I think we really need to figure that out. I think the EHRs help because you're getting some of these, you know, phenomenal EHRs in the middle of and hospitals all over the country, but are they just simply using the standard tools or are they able to? So I think that's one of the things that over the next year or two, a couple of years we need to actually think about as an industry is how do we make this something that isn't mainstream and we can all get a benefit out of it?

Kevin Erdal: Yeah, I couldn't agree more. And how do we learn from those that have had success, to your point, Andy, and also how do we think about the non-clinical spaces specifically as it relates to AI, which is where I get most excited. I think about the use cases around supply chain automating the PO process. We are, why are we hiring more people all the time to really process a purchase order? That's where a bot could really help, regardless of the organization. And it doesn't have to have the same level of rigor, if you would, around a clinical model itself.

Andy Splitz: Yeah, if you reduce the staffing on all the operational issues within a hospital that allows you to spend more money on the clinical and that's what we care about, the, you know, the clinical care.

Kevin Erdal: AI is not one size fits all specifically in the clinical space, no question about it.

Dr. Jerome Pagani: Let's go back to the strategy around data infrastructure and BI modernization. So are we looking at, to reference one of my favorite Tom Hanks movies, The Bachelor Party, Debbie, the car, right? One or the other? Or can these things be done in tandem?

Kevin Erdal: Yeah, they should be done in tandem, specifically from a strategy standpoint. So as Andy has talked about a lot, we're implementing the EMR in a lot of different areas, both stateside, even beyond. We have to think about though, why are we doing some of this, right? We're digitizing everything. We seem to have this obsession around digitizing everything specifically within healthcare. Super exciting. What is adjacent to that, though? Are we doing some of this to create efficiencies? Are we doing this to drive net new insights? Is it a combination of all the above? Oftentimes, yes, or at least that's the desire. So how are we starting to co-locate some of that data as we're sunsetting legacy systems? What are we doing with that? Because there's very valuable information. Do I even have the tools available or the competencies available within a given healthcare ecosystem to put dashboards on top of some very relevant data? And when I do and when we do, are we getting back into those that are making decisions, whether it's about the care that's being provided, about business operations, about staffing, which is very relevant in today's world, specifically around nursing and around physicians, what is that pathway? And the last piece I would just hit on is the training associated. So once we get the infrastructure dialed in, once we get the technology to surface some of the data, are we training those that are making decisions how to use this net new technology? We see it all the time where somebody saying, That's great, I have the infrastructure, love it, I have the BI tools, that's fantastic. I have this dashboard. I don't know how to take that action that we just talked about a little bit ago. So that's the other piece of the puzzle that we have to keep top of mind that while we're doing these things in tandem, which I believe is the right way to do things, we also have to make sure that we understand what the next three, six, five months and years start to look like.

Andy Splitz: Yeah, I agree with Kevin. It definitely should be done in tandem. The problem I think that clients are having is one, they have no money. Healthcare is not a, you know, budgeting really, you know, let's put it this way, healthcare is not in the business right now where they’re making a lot of money. Secondly, staffing is very low, so we don't even have enough people to do the care itself. And so what happens is, is it should definitely be done in tandem. The problem is, you know, how do I do both? Well, I may not be able to do both. So obviously the first thing you're going to have to do is put the EHR in because you need the data before you can go down in the data mine and then utilize the data for other things. The problem becomes and it's always something i've said for years and years is we put the EHR in, all the bells and whistles. Go to phase two, data innovation. All the BI goes to phase two. We finish phase one, then we go on phase one to another project down the road or put in a new hospital. And we never got to phase two. And so as Kevin said, it should be done in tandem. It has to be done. Honestly, I think the strategy has to be there, the budget has to be there. You have to plan on it. It may be consecutive, so it's not together at the same time, but phase two has to happen and it doesn't have to be the bells and whistles, but it needs to be the second part looked at as the second part of the entire implementation is not just, the EHR is being able to utilize the data, get the data out, and then also bring it back to the clinician, like Kevin said. So I think we would love it to be in tandem, but I think it's just not possible at times because no one has the money nor the staff to do it. And right now, you know, this is a very tight business. We don't have the knowledge base in the hospitals to be able to do it. And so I think, unfortunately, we're getting to a point where it isn't in tandem and then phase two stops. And so we've got to really push harder. So that Phase two is in five years down the road, because then we put in a $50 Million and we're getting nothing out of it.

Kevin Erdal: Yeah, sometimes the right strategy is for phase two, in your example, to start in 18 months. And that's okay. That's the strategy. That's the plan, right? So we don't have people saying where are my drive insights, Where's my AI while we're implementing an EMR? That may not be on strategy at all. That's coming in year X, so plan that out and be transparent about it.

Andy Splitz: Right. Exactly. You got to let your leaders know and your clinical people know because they have to understand what they're getting and when they're going to get it. And they can really, you know, plan out what's going on. I mean, you've got a lot of doctors going through physician burnout and stress. And literally people, physicians are leaving sites to go to other hospitals because of systems and things they're doing. That is, as Kevin is saying, that's a bad issue. You know, you shouldn’t be losing physicians because of a system, but you also need to give them the ability and the tools to take care of their patients easily, not at night, not doing the work with, I forget what they call it, pajama work?

Kevin Erdal: Pajama time.

Andy Splitz: It’s not supposed to be pajama time. So, you know, we've got to be able to get to the point where we are actually allowing them to do the job during the day.

Dr. Jerome Pagani: So, Kevin, Andy, you've already talked about examples of this, but I want to pull on this thread a little bit. How do we make sure that we are designing systems that work for the people who are both giving and receiving care? Kevin, you mentioned on the analytic side making sure that information is presented in context and you said, you know, particularly globally as we're beginning to implement these systems, making sure that the physicians and clinical care teams are really up to speed on what the system is doing and why it's doing that.

Dr. Jerome Pagani: And this is something we at Nordic are really passionate about. We wrote a whole book called Designing for Health. It's all about trying to make healthcare more human centric. What else can we do to really make sure that these systems are designed for people?

Kevin Erdal: Yeah. So my perspective is to your point and to your book, Jerome, pay attention to the why, make sure that we have a scalable and adaptable environment regardless of what it is, if it's an application, if it's infrastructure, or if it's an interoperability strategy, we know we're going to be presented with unique questions and opportunities five years from now. We don't know exactly what those opportunities are going to be or even what those use cases are going to be in detail. So we have to make sure that any technology, whether it's infrastructure or whether it's an API, is designed to scale and designed to adapt. And that way we can be prepared for what's to come next.

Andy Splitz: And so as Kevin goes down the technology route, I'll go back down the clinician route. Being a lab tech by trade, I'm on the clinician side. Honestly, I believe, you know, when we are seeing successful implementation in the VA and then and then other systems that are insulated to the hour, it really takes the clinical team to be involved. And, you know, again, I know all of our vendors want you to put in model or foundation or whatever it may be is as vanilla as possible. But again, if the clinicians aren't involved in the physicians aren't involved in the design of those systems, they're not going to be able to utilize them to give the best care possible to their patients. The goal here is for these systems to support the process of healthcare and the practice of healthcare is not to take over them. So you shouldn’t be telling, the system should not be saying, this is your what you need to do and go down this road and a programmer built it. It needs to be the care given by the physician. So I go down the road of let’s make sure that the physicians are involved and the clinicians are involved in these implementations, because if they're not, then the right data isn't where it needs to be. So as Kevin's talking about moving into the technology and utilizing the data, great. But if the doctor can't utilize it to actually treat the patient, then it's great that we can maybe moving into another system and use predictive analytics to it. But we missed the point. We're supposed to treat the patient. And so it needs to be both. It needs to be something that we can treat the patient, go back to discrete data like we talked about again, and then be able to also utilize it in other ways. And so I think that's where we have to make sure. On the entire implementations that I've been doing for the last 30 years, that has always been the key. It's got to be the people that are actually doing the work that are involved. They've got to buy into it, they've got to understand it, and they need to want to go down this road. And then that way they were successful and then we can tie in all the things that we need to because we've got a group that's actually going to use it and use it successfully.

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

Kevin Erdal: Yeah, a lot of fun as always.

Andy Splitz: It's been great talking.

Topics: EHR, featured, digital health, podcast

Module heading text

Get the highest quality chemistry and microbiology testing services aligned closely with current good manufacturing practices (CGMP) for all types of products across all phases of development.

Subscribe to receive blog updates