And really, you know, that seemed like the big thing at that particular point in time was the fact that, you know, you’re moving away from paper into electronic medical records. And then along the way was the problem was the fact that, you know, data wasn’t necessarily being stored in the electronic medical record in a discrete manner, PDFs and faxes still existed in the industry. And then everybody thought that, you know, just because you have an EMR, somehow everything is going to connect easily with each other, which didn’t necessarily happen.

So, it’s been an exciting journey. And in the sense that they’ve been a lot of progress made in the healthcare space around, you know, patient data, and around the availability of patient data to a patient, right? Because if you remember the old days, the patient would never get their own records.

And they would have to go through a tedious process to be able to get their own records to now where somebody can get into on my chart or some sort of a portal and be able to access their own patient data. Then the next issue along those way along that line has been around the fact that, well, I’ve been to multiple number of providers, why aren’t all of my records together in one place so that I can access everything. And then there’s been an issue around providers going, okay, I can’t access their data from other systems.

When people have gone ahead and solve that, then it’s come down to, okay, now that there’s data, there’s too much data. How do we make this usable? How do we make this manageable?

How do we de-dupe and normalize this data? So, it’s been an interesting journey. And now of course, there’s AI and the promise of AI to be able to kind of help some of these problems and solve some of these things around patient care and provider, you know, burnout.

So hopefully, you know, we’re on to the next phase over here. That’s going to be exciting around some of the new developments that are coming along.

Host: Yeah. Your journey really reflects how dramatically healthcare technology has evolved over the years.

Gurpreet: Absolutely. Absolutely.

Host: Yeah. So, moving ahead, the healthcare sector has spent years in making systems interoperable, yet providers still struggle to make sense of the data they receive. Where did the industry get interoperability wrong?

Gurpreet: So, I don’t think they got it wrong. It’s part of the evolutionary process, right? So most important piece of, you know, where we started off initially was the fact that everybody got into EMRs and really were using EMRs to be able to document and really manage patient care.

And then obviously, all of these were sitting on, you know, unconnected to each other. So, there was a need to go ahead and connect them. So, the first problem that needed to be solved was the fact that, you know, we needed to get all of these systems to be able to talk to each other and connect to each other, right?

So, which was point-to-point solutions or being able to send orders and results to the lab automatically or being then eventually evolving into a network strategy like CommonWell and Care Equality, eHealth Exchange. So, I think we’ve spent that first phase is established connectivity, right? Because connectivity was the big problem to solve.

And then once we’ve established connectivity, now the next issue is around the usability of the data because there’s a lot of data that’s coming from a lot of these systems. I was actually talking to a provider just recently who said that, look, when I am asking for any data on a patient, I get like 200 pages of data on a patient and that is like unusable. I can’t make sense of it.

It’s too much data. And it’s not in a format where I can immediately be able to affect patient care. So obviously, the evolution is now happening to the next phase, which is, okay, now that we’ve established a predominant amount of a lot of connectivity across the healthcare ecosystem, how do we now move this data from being accessible to being usable, right?

Because now we are able to access it. Now we want to move towards being accessible. Accessible has been addressed.

We’ve got to make it usable. So, I think that’s the next phase. And obviously everybody is talking about AI and innovation through AI.

There can’t be any innovation through AI if the data layer below the AI isn’t necessarily clean and holistic and longitudinal in that sense, right? So, I think that’s where we are. So, it’s not getting it wrong.

It’s understanding the evolution. And obviously things aren’t happening fast enough, you know, compared to the other industries in healthcare. So that’s where it seems like it’s not right.

But I think we are getting to that point right now where usability of the data is where the focus is. The quality of the data is where the focus is. And I think once we get that right, then obviously it helps open up a lot of avenues for a lot of folks out there.

Host: And surely you mentioned about a clear distinction between accessibility and usability of healthcare data. And which is important because it’s simply, you know, moving data from one system to another does not make it useful all the time. So, this brings us to how interoperability itself needs to evolve.

So how is modernized interoperability different from traditional interoperability methods? And what challenges or limitations of traditional approaches does it help overcome?

Gurpreet: Yeah. So, you know, the evolution’s been around the fact that, look, you know, we’ve been in the standard space, right? So HL7, and then there were different versions of HL7 and then got to FHIR.

And then once we got to FHIR and, you know, there’ve been standards around that, right? But standards don’t solve interoperability. Standards help resolve how interoperability can be handled, right?

But at the end of the day, you’re looking for, there’s a lot of data that exists within the healthcare organization. You’re looking for a way to go ahead and organize that data. You’re looking for, like I said, in the previous question that you asked me, accessibility was being solved for.

And now we’ve got to go solve for treating data as infrastructure, right? So just like we treat cloud as infrastructure or hardware as infrastructure, or, you know, a lot of the other infrastructure components that exist within an organization, we’ve got to treat data as an infrastructure. So, it’s foundational to anything that can happen within an organization.

It’s absolutely the base on which anything and everything can be built. So, I think we’ve got to kind of evolve to, so when I said, when you’re saying traditional interoperability was just connect one point to another point. And I think the evolution now is, okay, we’ve got to look at it more as a data orchestration sort of mechanism, not just a pure accessibility thing.

It’s really treating data across the entire organization, whether it’s legacy, whether it’s lab data, whether it’s a pop health-related analytics, AI interface engine, you name it, right? We’ve got to treat it like infrastructure. We’ve got to treat it as a baseline.

And then you need some way to make sure that you’re orchestrating this data in such a way to make it usable, to make it helpful to be able to take care of patients. Because at the end of the day, healthcare is about patient care. All of us, you know, you and I are patients and everybody that’s listening to this podcast is a patient, right?

And at the end of the day, it’s about taking care of the patient and having the right data at the right time for the right reasons, you know, to the right people is absolutely essential for us to really make any sort of progress in terms of providing better care.

Host: Totally agreed. And taking that idea from industry level to the real world, we move to our next question. So, ELLKAY connects over 750 EHR system and 58,000 practices while offering sophisticated data orchestration services.

What are the key driving factors that make such an impact in the highly competitive industry?

Gurpreet: Yeah. So, it starts off with the very basic concept, right? Every one of these systems that we connect, you know, guess what?

There are about 750 EMRs and practice management systems who knew it, right? Everybody knows about the big ones, but they are that many. And the biggest challenge then is the fact that look, all of these systems have their own data model, have their own sort of the way the data is organized in it.

Biggest challenge is to go ahead and create a standard data model that maps to all of these systems out there. That’s something that ELLKAY has been able to do. We’re the only organization that’s been able to map over 750 different EMRs and practice management data models to a standard data model, which is a big deal.

So, the point is that, look, ELLKAY has been in the industry about 22 to 23 years out there, right? And we’ve started off in the lab space with autism results. We’ve been able to work in the data archiving and data migration space with a lot of the acute and the ambulatory EMRs.

We’ve been able to help with the payer data exchange. And then we’ve been able to do a lot of things around interoperability. And I feel like all of that coming together really is about now taking it to the next level in terms of being able to make that data usable, making that data accessible, but making that data help take care of patients at the end of the day.

And that’s what data orchestration is all about is the fact that, you know, whether it’s archived data or whether it’s pop health data or whether it’s data aggregation for analytics or AI, or whether it’s interface engine or whether it’s lab integrations and our payer value prop that’s being provided to them, you need a platform that goes ahead and really, you know, orchestrates all of this to make it all connected to also make sure that the value out of it can be really accessible to the organizations and to the stakeholders within the organization. A lot of people don’t know this, but CommonWell Health Alliance, one of the largest networks in the U.S. runs on ELLKAY, right? ELLKAY runs that platform for CommonWell, and we do about 15 billion document exchanges a year.

So, it’s a pretty significant amount of work that we’ve done out there. So that gives us a lot of sort of domain expertise, domain knowledge as to what we’re seeing in the industry, and really in terms of helping further the entire concept of efficient and optimized patient care through accessibility and usability of the data that’s available in the systems out there.

Host: So, I could trace some elements from your response that are accessibility, connectivity, data mapping, and orchestration, which are certainly important, but they are only the part of the larger picture. Focusing on that, ELLKAY has moved from being known primarily for interoperability to a broader healthcare data orchestration strategy. What convinced you that connecting systems was no longer enough?

Gurpreet: Yeah, and I’ve already answered that, Rittika, right, in the previous thing, previous discussion is the fact that, look, just because we’ve got connectivity doesn’t mean that we are now, you know, actually efficient, right? So, efficiency and usability of the data is where data orchestration is all about, and really bringing down costs. You know, think about it, right?

Organizations in the past have spent a lot of money with different vendors for each of those different initiatives around handling data within an organization, and yet they’re not connected, right? So, they’ve sort of solved all of these in silos, but they don’t have a proper end-to-end data workflow that exists within the organization. They don’t have the ability to make sense of all of these initiatives across one simple dashboard, across one simple sort of platform, right?

And that’s been the biggest play for ELLKAY is the fact that we’ve built all of these pieces out there. We’re probably one of the very few organizations that understands data at the level that we do, and then it makes sense for us to basically pull this up and say, hey, you know what? All of this data exists within the organization.

There’s a need for somebody to go ahead and make some sort of an order out of the chaos, right? There’s a lot of chaos in that data to really bring some sort of a sense of discipline, some sort of an understanding of what this data is all about. And really, at the end of the day, it comes back to the same thing that I say all day, right, is the fact that if that data can’t be helpful to the provider to help with patient care, then that data is pretty useless, right?

And that’s where data orchestration really plays an important role.

Host: I think it is all about making data useful and effective.

Gurpreet: It’s not a simple task. It requires somebody like ELLKAY with the experience that we’ve had over the years to be able to do that.

Host: Yeah, that’s great to know. So, moving ahead, you have described the unmanaged gap between IT systems and data silos as one of the biggest healthcare IT’s hidden costs. So, where do organizations underestimate that cost, the data cost the most, and what does it actually look like on the ground?

Gurpreet: Yeah, so it’s really interesting, right? I mean, just give you a simple example of an organization like MultiCare, right? MultiCare out of Seattle, out of the Washington area, they’ve got about 12 to 14 different hospitals.

They’ve been acquiring hospitals in the area over the last few years, right? And they recognize the fact that, look, and it didn’t make sense for them to be looking at data at a level where you had different vendors for each of those different silos, right? And then when we got engaged with them, they realized the fact that, look, this has a cost benefit around the fact that if we can consolidate our vendors, if we can consolidate our vendors and really have one vendor that understands the entire end-to-end data workflows and also the data, whether it’s legacy or whether it’s for pop health, like I said, or for analytics or any of the other initiatives within the organization, that what it does, it brings economies of scale, right? So, a lot of times people are thinking of data when they have an initiative out there.

When a new initiative comes up, they think, okay, you know what, what am I going to do now? How am I going to get access to the data? But if you solve that at a basic level and have a foundational sort of data infrastructure in place, which is what MultiCare is really going about achieving with ELLKAY, then what it does is you can now very efficiently go ahead and develop your new initiatives and be able to have them up and running really fast, right?

So, there are three components that are most important in healthcare, right? It’s speed, which means being able to do things really fast, being able to connect fast, being able to make sense of the data really fast. Scale, being able to do a lot of it very fast, right?

Across millions of patients, across a lot of data points that exist across various health systems and other data sources out there. And then the last thing is value, right? If you’re not able to provide value, value is just not cost.

It is cost, but it’s also value in terms of, is this going to really help with patient care? Is this going to be able to help with preventive care? Is this going to be able to really enforce providers get 10 minutes with a patient?

And in those 10 minutes, they’ve got to be able to provide a lot of information and or be able to access a lot of information and make sense of what that information is telling them to provide that sort of better patient care. If there’s a way for that to be done through optimized use of data, then that enables in reducing the cost of patient care, reducing the cost of IT systems out there, reducing the cost of also healthcare in general, if data is accessible at the level that we hope to be able to make it available through our data orchestration platform.

Host: And that was fascinating to know. And this becomes particularly relevant as healthcare organizations increasingly turn to AI. So, addressing that, everyone wants to put AI on top of healthcare data, but you have argued that the data foundation has to come first.

Are healthcare organizations rushing into AI before fixing the data it relies on and what will that mistake cause them?

Gurpreet: Yeah, I don’t think they’re rushing in. I don’t think that’s the issue. I think the bigger issue is underestimating the value of data in their AI initiative.

So, when anybody thinks of AI initiatives within the healthcare organization, they want to be able to make better clinical decisions. They want to be able to get into better preventive care. They want to be able to improve their billing at the end of the day.

All of that’s going to require good quality data, but more importantly, holistic data across all of the data sources. So, I think they have to look at it as an equal, given the same amount of importance as they do with AI initiatives that they have. Just because somebody says AI doesn’t make them innovative, just because somebody putting together an AI initiative doesn’t mean that it’s going to be successful unless the data infrastructure issue is solved at the basic level.

Ultimately, the LLMs exist. There’s a lot of information that’s available on how to go ahead and make use of the data. But if the data itself isn’t very usable or accessible, not necessarily accessible, but holistic in that sense, it isn’t providing longitudinal view and isn’t good quality data, then all of those initiatives aren’t going to be as successful as they’re hoping them to be.

And so, I think data plus AI initiatives is equal to innovation. So that’s the way I think people have got to look at it.

Host: Yeah, AI can probably advance data management, but responsible AI usage is also important here. So, moving ahead, ELLKAY recently revealed that healthcare organizations that orchestrate their data cut costs by over 60%. How does this work? And what other benefits does healthcare data orchestration offer in the upcoming years?

Gurpreet: Yeah. And I think that’s the tone of this conversation we’re having over here, right? Like multiple vendors handling different silos of the data within a healthcare organization means extra costs.

And even then there isn’t connectivity between all of these data silos, right? You still got to figure that piece out and be able to solve for that. Consolidation with a single vendor like ELLKAY that understands data end-to-end and can really provide that data orchestration governance layer on top really goes ahead and brings down the cost tremendously, right?

We’ve seen up to 60% reduction in costs within healthcare organizations while improving efficiency by double or triple, right? In terms of the efficiency within the organization. So folks have actually seen that their backlogs of data acquisition or interfaces has reduced tremendously.

They’re able to connect to different parts of the healthcare ecosystem a lot faster. They’re able to go ahead and then make use of that data a lot faster, but be able to kind of, like I’ve mentioned before, build the foundational aspect of data on which they can build their today and their tomorrow’s aspirations and be able to get those up and running really quick, right? So, across all of this, you know, organizations have seen at least 50 to 60% reduction in costs, which is a big deal considering the fact that healthcare organizations are struggling with budgets right now.

They’re operating on 2 to 3% margins. So, it’s becoming a major problem, right? And so definitely I have been helping with healthcare or working with healthcare organizations to help them reduce their costs tremendously.

And at the same time, be innovative and be ready for the future.

Host: Yeah, that gives a clearer view how, you know, data orchestration offers a holistic approach to manage data. And I think ELLKAY is doing it just right.

Gurpreet: I think so that we’ve had a lot of experience, right, Rittika? So, it’s been a tremendous amount of experience doing this. So, we feel like we are onto something and we feel like we can help our healthcare organizations be successful.

Host: That’s how expertise pay off, I guess. Thank you, Gurpreet, for joining us and sharing your perspective on healthcare data, interoperability, AI in healthcare data and data silos. It was indeed a valuable conversation.

Thank you so much for joining.

Gurpreet: Thank you for having me. I appreciate you taking the time to talk to me.

Host: Also, we appreciate our viewers for accompanying us in this session of ExtraMile WisdomPlexus. We’ll be back with another informational discussion soon. Until then, stay tuned.