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March 11, 2024: In this Interview In Action double feature Sameer Sethi of Hackensack Meridian Health and Jason Rose of ClearSense explore the topics ranging from AI data organization analytics to improving platform technology in healthcare. How is Hackensack Meridian leveraging AI and RPA to predict patient needs and improve decision support within the clinical workflow? What role does Google's cloud platform play in accelerating access to vital health data? Then, Jason Rose addresses how ClearSense enables significant cost savings and operational efficiencies by streamlining data management and supporting value-based care initiatives. Dive into a discussion that navigates the intersection of technology and healthcare, revealing practical applications of AI that promise to reshape patient care, operational workflows, and the broader health ecosystem.

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

Welcome to This Week Health. My name is Bill Russell. I'm a former CIO for a 16 hospital system and creator of This Week Health, where we are dedicated to transforming healthcare, one connection at a time. Today , we have an interview in action from the 2024 conferences, the spring conferences, VIVE in LA, HIMSS in Orlando.

Special thanks to our sponsors, Quantum Health, Gordian, Dr. First, CDW, Gozeo Health, Artisite, and Zscaler. You can check them out on our website, thisweekhealth. com. Now, onto our interview

β€Š πŸ“ Here we are at VIVE 2024 in LA. Beautiful LA. Sameer Sethi with Hackensack Meridian. Chief Data Analytics Officer. You're probably like everybody else. role is more than that. Yes,

absolutely. So I help lead the data and analytics team. I also have a group of folks that do robotics process automation.

And then now, with AI being so prevalent, we've been focusing on building AI use cases for the health system. So just

Data, RPA, and AI. Just those three things. It's interesting because there's so much technology coming at us these days and it's dramatically changing all the different things that we is RPA and how is AI changing the world of data and

analytics at this point? That's a great question. So at Hackensack we actually call it IPA, which is Intelligence Process Automation. What we have done is we have taken Robotics Process Automation and AI and MERS together. The way we generally look at it is, first of all, we never start with the conversation of AI or RPA.

We always look at a problem and we say, how do we solve it? And there are various technologies that help. Sometimes it's just data, sometimes it's analytics, sometimes it's AI, and sometimes it's RPA. But lots happening here. There's HMH is very bold in making moves around, building partnerships, for example, Google.

And then also working through folks that have We've done this for a living. We realize we're a health system, we're not software developers per se, so some of it we build ourselves, but some of it we partners with vendors, something they've built or pre baked and we bring it in house and we make it our own.

Give me a little of what you're doing with Google at this point.

Yeah, absolutely. A couple of things. One is, our data and analytics stack exist on Google today. We started this journey two years ago where we started to take all our data out of on prem and move it on to a GCP. What that allowed us to do was You know, being able to scale these use cases and go to market a lot faster.

The concept is, if in the past if I needed a high compute environment, I'd have to go buy a server and put it in. Versus today the cloud technology, you swipe your credit card and that becomes available to you. But in addition to that, I think Google has a lot of, they're very bullish on healthcare, right?

So we have their attention, they have ours. So are innovating in various different ways. We're in the process of building a research This is something that that the HMH leadership and the Google leadership came together. And we felt that while we have a data analytics platform for the non researcher the researcher's need were a lot more specific.

Cohort discovery, for example trials matching. These are the things that are very unique to researchers, and Google and other partners actually helping us with that.

Your data has been aggregated into the GCP platform. Yep, that's right. That's the GCP platform.

How do your academic, medical, your specialists, and your researchers access those tools? Is it through traditional like R and those kinds of things, or is it

special set of tools? No, it's not special. We have R and Jupyter Notebook and those tools available as well. In addition to that, we have Google specific tools as well.

The way we think about making tools available is through a workbench. The workbench has various different platforms. Google's capabilities, we have R, we work with a company called h2o. ai, which is a low code, no code platform that allows data scientists to focus on building and training models versus actually writing code.

So a mix being able to, provide tools which allows folks to work faster.

So what are some of the outcomes? you been able to enable within Meridian? Yeah,

so really good use cases. we think about, high end analytics or AI, advanced analytics, there are three buckets that we focus on.

One decision support. This is where, whether it's a clinician or somebody in operations, we're giving them the insights.

So you're delivering that straight into the EHR and the workflow? We are,

yes. And I'll talk about that in a bit. So decision support is one bucket. The other is where we are optimizing workflow or helping folks.

And the third is being able to interact with a patient using these technologies as well. The patient piece we are slow on. We feel that AI And our governance process around that would take some time to mature up as well. But for disease prediction, we have done quite a bit. We have built a couple of capabilities.

CKD, chronic kidney disease, where we are predicting or detecting chronic kidney disease much earlier than what we have seen usually happen. In addition to So that end of life care, palliative care is being one example of that. We have actually built capabilities by which we are predicting the need of that care much in advance of what we generally see physicians realize and that pops up within the clinical workflow within Epic as what's called a BPA.

So the workflow is a physician goes in and or she is prescribing medicine and as they say place order, a BPA pops up and it shows you should consider this patient Palliative care, the chances of this person, getting into end of life care is an X percentage. And then you click the next button and all of a sudden an order is created for someone to show up the next day and talk about palliative care.

So we have focused on decision support a means of enabling physicians. So it's, difference, right? In non healthcare world, it's easier to deploy AI directly, whereas what we do is that we keep the human in the loop and help decision support.

Yeah, that a lot of sense. How has the traditional report, like I remember back in the day, that team used to come to me every year and say, Hey, we need five more people to do the reports. We need five, five, five. And it just like compounded. I'm like, well, what's going on? And we would just have all stale and old reports. Has that evolved?

It has. So when I inherited this team, we had close to, I don't know if all of them are considered dashboard, but we're close to 17, 000 assets. In our library. A good portion of them weren't even getting used, to your point. So what we're now focusing on is, we focus on the data and creating different maturity levels of data.

And we're bringing technology to sit on top. A good technology is natural query language, right? This is a technology that we are very keen on deploying where you or somebody as a person that doesn't know how to code, writes the question in Now this is not generative AI which sounds like it, and natural query language which actually translates that into an actual query that hits against the database.

So what we are doing is, we are removing the analyst out of the equation, and putting technology to replace the analyst, in most cases, so a person that's not technical in nature can interact with the data directly, right? Now this happened dashboards, that's what was going on, but dashboard is a very curated view, which people don't get to change.

They can change the date and a few other things. But in this case, you are asking questions. and you're getting an answer. that's where we are moving towards, which is removing the analyst in most cases and providing direct access to people, direct access for people to into the data. Natural language?

Natural query language. So what it is, it's you write it in English, how many COVID patients do I have at JFK Medical Center, right? That turns into an actual query in the back end, hits the database, and it gives you a response.

Talk to me about the aspect of data because this is a significant conversation that's happening all across the industry right now, which is how do we become more effective with the resources that we have?

How is data being utilized and how is AI overlaying that, giving us more insights into how our resources are being used within the health system? Yeah, so

I think, based on everything that's happened in the last year and a half the generative AI and the push towards AI.

Things are becoming a lot easier, like our teams are able to actually produce more use cases because the hyperscalers are coming in and helping with that. A good example I'll use is large language models. Like, no one knew what this thing was a few years ago, right? I know, it's crazy. And now all of a sudden we have access to it, right?

We have an API we can call tomorrow, right? And while niche use cases are being able to address in that order, but some of them are. So I think what we're So what we're able I think the hyperscalers are investing in it and providing, for profit obviously, capabilities that improve speed to market.

So that's something which we have seen, so my teams are actually pushing our use cases a lot faster than we did before.

One of the things that people get concerned about is if I pick a certain I was going to say hyperscaler, but just say big tech partner. You guys have selected Google.

Does that limit you from Using the other big tech partners

that are out there. That's why we selected Google. So if you look at what Google did at Google Next last year, they said you'll have the Google large language models and you can bring your own as well. So that's one of the reasons of us, relying on or creating a partnership with Google was because they're open to actually bringing in non Google assets so we can actually use it in our ecosystem.

That's exciting. We will have to stay in touch. I want to keep hearing how you guys progress. Samir,

thank you. Yeah, thanks for your time. FHIR.

(Transition) β€Š πŸ“ πŸ“ πŸ“ ​

(Interview 2) β€Š πŸ“ alright. Here we are from five 20. We're down here in LA. I'm joined by Jason Rose, CEO of ClearSense. Welcome to the show. Thank you. forward to the conversation.

We've talked at ClearSense over the years about different things. I want to start on the foundational level. A lot of health systems that are struggling, I think we can say struggling financially. It's been a of years. Duffin Hall reports just show it's getting a little better.

But a lot of health systems are looking forward. efficiencies, they're looking for savings, and, ClearSense really starts at that level and then moves up from there as a platform. Talk a little bit about how ClearSense helps organizations drive that efficiency.

Yeah, absolutely, and thanks for the opportunity, Bill.

I'm really looking forward to this conversation as well. ClearSense has a great background in success where we work with large health systems and regional health systems alike who have had challenges in governing their applications that they need to archive and still have access to it. And it's interesting.

I was talking to someone this morning about how difficult it is to really do a program that looks at the details of what are the applications that are running today, how much cost do they have, what's the value they're creating. And especially when you're looking at large organizations that have mergers and acquisitions.

These health systems aren't necessarily focusing on how they shut down applications that they no longer need to have in production, so to speak, but they do need to have access to it for both legal and clinical reasons. And so being able to do that in an efficient way, but I'd say there's lots of companies out there that look at a few applications at a time.

I'm talking about potentially hundreds at a time, looking at tens of millions of dollars of cost reduction on an annual basis. That's real money and it's just waste. And it also limits their risk as well in terms of exposure because those applications and that data really needs to be secure. And so it's really important to find an enterprise wide class tool that can actually do that at scale and also partner on a governing and strategic side as well.

Well, I like the fact that you're starting at a very base need for an organization and then it grows from there and it becomes a data governance solution. It

pays off. These four, the EHR systems, it is the ROI. It funds the EPICS and the Cerner migrations.

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β€Š πŸ“ πŸ“ πŸ“ We have a pretty well run shop.

And I remember when we did the EHR implementation, they said, hey, we have to keep this thing on for six months. And then like a year later, it was still on. That happens a fair amount healthcare. And it's important to turn those things off because you don't get the ROI on the original project that you want.

But one of the challenges of that is The reason they ask for it to stay on is we still have to process transactions out of it. But you guys have the ability to pull that data in and process those transactions.

That's right, and partner with usually the IT organization but across the entire enterprise to identify what are the reports that you need to have, how do you recreate these, but then that's on the regulatory HIM side, but on the clinical side, those clinicians at the point of care also need to have access and be able to visualize the data.

In the point of care, so be able to click on a button inside of Epic and have a access to a rich, robust system that can provide data visualization for all the clinical reporting so they don't have any problems with the continuity of care.

So one of the misnomers is that all the data is in the EHR, we don't have to worry about anything else.

But this is where a platform like yours comes in because there's any number of other systems that are out there. But in order to deliver Value based care. In order to integrate with the payer provider, in order to do all those things, you have to get to a platform. Otherwise, you're creating, a jillion different interfaces, I would think, out to all these different platforms.


I think, Bill, it's important. So I spent the first 10 years of my career on the provider side of healthcare tech. In the last 20, it's been on value based care. I'm more focused on the payer side, enabling clinicians with the right data to drive value based care outcomes. And to me If you think about that foundational aspect of looking at the legacy data of applications to be shut down, well, you take that same model, but you leverage the same technologies to make it more real time information and then allow, whether it be the health systems the, and or the payers to build their own applications on top of that common data management platform.

And so if you think about, the last 20 years, so I started in value-based care in 2004 when that wasn't even a term. And so So do we attribute

the term value based care to you? No,

I definitely not. I will not take responsibility for that. But I think that, so you think about why is value based care not taken off?

There's lots, you can put some statistics to say that there's been some good use cases. I'm not saying there hasn't been anything, and certainly I've been a part of a lot, but it's still very immature. And to me, the opportunity is converging the payers and providers through the data lens.

Because why is there distrust? Why is there challenges between payers and providers? I think it's the data. It's the completeness of the data. It's the accuracy of the data. And it's the timeliness of the data. And so if you can fix that, so that you have a common data management platform, which I, in our case, we think ClearSense is the best data.

It exists today really not hot out there focusing on this, but merge at both data sources, clinical data, robust data, not just the epics and discerns, but all the ancillaries as well that are relevant. And then also all the payer claims data is relevant. Give them a common platform. What about social determinants data?

Absolutely. Absolutely. So

you're bringing it all in, giving that complete picture of the patient and their journey.

And then that's how you can really deliver value based care and trust and audit controls. So that you can converge payers and providers together. Even the payviders have problems in their own systems.

So let alone if you're trying to merge larger carriers, publicly traded insurers, the blues, with their provider clients. They're challenged because the data doesn't allow them to. And so having that auditable, accurate, timely, robust data set, well then they can build on the accountable care applications on top of that.

I think is really critical for the next steps in value based care. It's interesting,

so you have this platform that allows you to do data governance, allows you to do transformation of the data if it's necessary keeps a history of the data so you can see the raw data all the way down to any changes that have data.

All those things are so important for the physicians to know, hey, what's the quality of this data that looking at? the concept of getting it to the point of care. And I think this is where people say, oh you have to do it. It has to be the EHR. But you guys have tight integrations with the EHR to go out and grab that data and pull it in when it's


That's right. Yeah worst thing you can do to a clinician is say you have to go into another system and log into another set of credentials within the system to get to the information. And if you're in an emergency room or surgery, that's just not going to happen. Having it embedded inside the EHR itself so they can pull the Information in real time have it be presented in a way that's meaningful for them is really important.

So it's not just legal and regulatory, it's also from the clinical side. And then I'll go a step further. If you think about from my payer lens as well RADV is gonna be much more important. There's a lot of CMS focus in RADV for risk adjustment. And certainly star ratings and HEDIS is always a challenge to get to the right data.

And the clinicians, for decades say, I've already done that. It's already in the EHR system. Well, the payers can't get to it or they can't get to it meaningfully or completely. And CMS is going to seize on that and look for opportunities to say that the data is incomplete. And that's a major problem from a regulatory compliance for the payers.

The clinicians don't and the hospital systems don't want more noise and more applications that are IRIS FHIR,

FHIR, How does and contrast with something like Snowflake or some of the other platforms that

are out there?

We're a healthcare company. So we're Healthcare Tech. We started as Healthcare Tech. We are focused on Healthcare Tech. So we have a deep experience in terms of the models themselves. So we're not just looking at data through the traditional sense of FHIR and things That nature of more new, but we're also right going to the data model itself and going right to the database and a database is a database.

So because we have such rich experience and Cerner's and epics and all scripts and Athena's of the world, and we also understand the claims, medical pharmacy eligibility claims on the payer side, being able to converge that in a meaningful way and combine that is really different. And as much as I, I love the snowflake.

You've seen one after another for the last 20 years. You've actually tried, look at Haven. Just name your choice. They don't understand healthcare enough. And they don't start with understanding a 4 trillion industry. Whereas I think a healthcare company and technology is critical.

So I was one ClearSense's first clients at St. Joe's. And it was interesting to me. We were looking at this platform concept. Cause we had a, cause I play a great game. We had to bring all this data together in order to deliver on a clinically integrated network.

It's essentially a value based care model for us. And we couldn't get the data. Like, we couldn't get it to the point of care. That was the first thing. But the second thing we found after we started aggregating all the data, consolidating applications, aggregating the data, is it became an interesting platform for us to build.

And I'll be the first to mention the term that must not be mentioned, which is AI. It became a good foundation for us in 2015. How are we gonna tap into these AI models that are coming in here? And at that point it was some clinical decision support. It was some things around scoring and other mechanisms that we were using.

But then we were, we were trying to pick it up. That's the distinction of a platform, right? It's, get that first thing and that second thing and then all of a sudden you're sitting back going, I think we could do this with it too. We could do

that. It's an enabler. So data is oil, right?

And you've got lots of oil fields in health systems and in payer land as well. But you have to figure out a way to mine them and get good quality information that you can actually now enable additional AI applications, business intelligence, whatever the case may be. You look at companies like a Salesforce who built a platform as a service, but they don't, really didn't come from healthcare.

Having a model that provides robust, quality, timely data and you build those applications on top of that. So I think whether it be a health system or a payer or frankly other vendors, building platform applications on top of the data management is really the only way we're going to move it forward because it's just too costly to have inaccurate data and also data that's not widely available.

It's You have to be able to trust it. Fantastic.

Jason, thank you for your time. Thank you, Bill.

Appreciate it.​

β€Š Thanks

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