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  📍 I'm Bill Russell, creator of this Week Health, where our mission is to transform healthcare, one connection at a time. This is an executive interview

quick powerful Conversations with Leaders Driving Change. So let's get started.

Alright. Here we are from the HIMSS floor. It is Wednesday morning, and today I'm joined by. Jason Rose and Jonathan Cook, CTO. Clear sense, CEO. Clear sense, right?

Yep.

Oh yeah. I'm reading this press release. It's really interesting. Can I

see a business card?

Was uh, was this uh, press release today or?

I was on Monday.

On Monday, yep. Alright. This is really interesting to me 'cause we've been talking about this on the show for a while now. In, In that AI's gonna require a. A platform for all this disparate data we have. You know, epic is sort of, sort of establishing themselves as, Hey, we're the clinical, the problem with that is they're not all these other things that we need.

We need to create these unified workflows across the entire health system to do all the things that we're doing. And I'm reading this press release. I'm kind of excited about it. 'cause I think you guys are getting pretty close. So Jonathan, we're gonna start with you.

Sure.

Just to level set everybody, what are the pillars of the press release?

How is AI being integrated into this? It's not an enterprise data warehouse, but for lack of a better termin terminology, it almost is,

well, we jokingly call it a data swamp, not a data lake. 'cause we get data in any format that you can possibly imagine. But the three pillars are acceleration, the ingestion.

So how do we get data into the swamp? Once we have the data in the swamp, you have to do mapping and cleanup of that data. Qa. How do we do that faster? And we're using AI techniques to do that. We have models trained and we have workflows that have worked across multiple different data sources that, that help our team do that.

I'll be very, to be very transparent, there still is a human QA process afterwards, right? Or, you know, people are very worried about deterministic versus non-deterministic workflows and what the logic's doing. We apply deterministic based on the client's own qa.

So let's stop there for a second because, we didn't really level set this. We've done so many interviews. I'm familiar with what Clear Sense has done. Sure. You guys are a, an engine for application rationalization,

right? Yes.

And and you have literally archived thousands and thousands of healthcare specific applications.

Yeah.

Over the years. So this ingestion process. Putting AI around it, accelerating it, especially at a time where money is tight.

Right.

This is real dollars.

Yeah. We're already, we already known as the largest and most impactful a PM solution.

That's a class uh, press release. And also in the Gartner with the Trinity story in the case study that I was just on stage at the live event with the Trinity folks. So we're already established that we're the largest and most impactful this ai, which we've been. Using for years. But we've really uh, as I was uh, saying earlier, hardened our AI strategy.

This first part that JC was describing is just going to the next level using AI to just that much faster. You know, as a software development firm we are employing the AI to really drive that designated record set so we can minimize the amount of human touch and speed the impact much faster.

Well, this is where it gets exciting as a pillar number two gets gets me going. So talk to me a little bit about

Sure. I mean, I think one of the core things, just to build off of Jason and take you right into pillar two is, you know, in the archiving world quite often it's body shop, it's, you know, like figuring out ad hoc how to take care of that old system to pull it in.

And what we're taking is a very software centric approach. We are becoming a software company. That is figuring out how to do this faster with ai. And so like the second pillar is I am a clinician or I'm an admin in a organization and I pull up an archive for Bill. There's 160 documents there,

right?

It's P-D-F-L-I.

How do I make sense of that Quickly? I don't have time to read 160 documents and scroll through them. We're doing a summary of that quickly based on your context, so that quickly tells you. We call it a cheat sheet, but it's a quick summary across those documents, if is what you're looking for even in there, right?

And then helping you focus to get to where you need to go. And then where it gets really interesting is bringing that back into the workflow of care, right? So at the point of care, you're in the EHR looking at the problem list or whatever marker you wanna set up, it gives you the ability to go, okay chest pain in the problem list.

Is there anything in the archive that might be of interest that we should surface quickly and that, that enables you to quickly find out 160 documents, what you need to look at

as a part of the longitudinal record? Yeah, so it's really converging the live EHR data, with the archive data, and then make it much faster and better clinical quality of care for the clinician at the point of care in, in, in the actual EHR.

The the big move that has happened here is we're taking all this unstructured data. And we're giving you the ability to look at it as, as almost as if it is structured data

Yes.

As quickly.

Yeah. You nailed it. That's it. Absolutely.

Have we moved to markdown files in this? I'm just curious if we're heading in that direction.

I, we are. I mean, there's some parsing and there's some tricks that you have to do with it when we get to that, but Yes. Yeah. I mean, the PS everyone's ripping through PDFs with AI now.

Yeah. It's really interesting. Third pillar.

Third pillar is where I think it's really interesting. Yeah, I agree. So, so the third pillar is let's, before I just tell you what it is you're, you're an archiving company.

You have 800 plus systems over 15 years sitting in your data swamp, right

across all the disciplines.

Clinical, financial, hr, faxes, employee notes, images, everything. Images, everything. And so everything. Yeah. That is an amazing piece of data as a whole to start building an AI foundation. To understand your organization, we jokingly call it a time machine.

We're building a time machine. We're giving you the ability to go across time, across the entire organization, find correlations across operations in clinical using ai. So the idea there is that we are able to take your data and allow you to have conversational or structured queries using ai. And here's the important part, is that a lot of people are very, very nervous about putting AI on HIPAA related data.

Right. We enable using private models for you to be able to do this. You are not sharing your data out with open AI or with Anthropic. We are building out a pri private model. It's a couple dollars more for you to get it, but you have a private model and then the, the, the real, real power is then you can then pay extra and we can fine tune that for you on your data.

That's amazing. The you know, when I think about we've been talking about, you know, data is. Oil is, you know, the we really believe that is true. I mean, when you, when you can start to look across years and decades of information and start to see patterns, start to see readmission rates, start to see, I mean, I'm just talking about the clinical side.

Then you see denial rates and you see, I mean, that information, I don't even think we understand how much is there for health systems yet.

I think we've only tapped the desert at this point. We don't know what's under the earth. Yes,

it's the memory of the health system.

It is.

That's why we call it a time machine.

It's a time machine. It gives you the ability to go back and it is the full memory of that organization. And I mean, quite often, you know, we're here at HIMSS and we're seeing a lot of people putting AI on the EHR and on the clinical system. We are looking much larger than that. In fact, 60% of the data in all of our clients is not clinical data.

Yeah,

it's operational data.

Alright, so let's go through the three pillars again. Sure. Just to summarize this, so. Go ahead. Sure. Ingestion.

Ingestion. How do you do ingestion faster To Jason's point, every year that you leave that app sitting out there, you are losing money. You shut it down as fast as possible.

So our goals enable you to shut that app down as fast as possible, reduce

your license costs,

reduce your licensing, reduce your cyber risk. Yeah, cyber risk is something, you know, Drex is around here somewhere, but you know, I'm sure Drex is gonna write an art. Drex is over there. Write an article on this, but.

Cyber risk. Cyber risk. People are focusing on the licensing. But if you look at your cyber risk in some of these, or

it's part of your attack service,

Oh I'm sure it was on the latest operating system.

Yeah. Right. It was

patch.

It was patch, it was

patched up to 2 20 10.

So again, pillar number one, get the data in faster. Use AI ingestion. Pillar number two, with context within the EHR, your workflow, how do I enable you to find the data that you need based on your context, either the EHR or your, your uh, own search. And then the last pillar is you're sitting on a mountain of data.

How do we turn that into an intelligence for your enterprise? In archiving, we have clients that have 400, 500 systems, actually 800 plus systems, and applying AI over top of that to be the memory of your organization. And become a time machine

and a private and

a private LLM if you're worried about hipaa.

Yeah.

That's fantastic. Jonathan.

Thanks Bill.

Fantastic work. Thanks Bill. Appreciate it. Appreciate it, z Appreciate it.

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