🔥HealthMosaic Just Fixed Healthcare Interoperability - Here’s How

Speaker A: The Health Mosaic is what we're coining as the first clinical AI marketplace. The AI landscape now is only going to double and if not triple that complexity of having to connect multiple modalities and point to point solutions. Your journey, that could take 18 to 24 months or longer, and you're burning dollars and not getting to the endpoint to AI that you could or couldn't use. And it's more of R and D than an actual outcome application for clinical use.

Speaker B: Hello everyone. Welcome to one more episode of Unlocking the AI Advantage podcast and also simulcast on YouTube, so video as well. And, uh, this is your host, Ramesh Danta. And today, uh, we're going to have an interview with, uh, the CEO of a company operating in the healthcare and AI space. Uh, essentially those are the two areas that I cover, health tech and AI. So we have a CEO of today that's operating, uh, in those two areas. So without further ado, so let me actually bring into Justin Ramsaran, who is the CEO of HealthMosaic AI. So Justin, please go ahead and introduce yourself and then later on I'll get into what Health Mosaic does.

Speaker A: Yeah, absolutely. Again, pleasure to be on the show with Ramesh. Again, the opportunity to talk about digital health and AI and all the fun collaborations going on in that current space. So a little bit about myself. Name is Justin Ramsaran. Um, I am by trade a engineer, software developer. Biomedical engineering was my background, um, from there transferred and then ended up towards more of the business sector. Really wanted to be a doctor, wanted to be in medicine, um, and just found a different route. Right. To still impact and help empower patients, lives and the care downstream, um, in the current space that I'm in. So, digital health, did you actually start

Speaker B: on the medical, uh, journey, uh, then left midway?

Speaker A: I did, yep. Yeah, that's exactly correct. So I did, uh, go through the process, took the MCATs, did the exams, got into med school, all the fun parts. And unfortunately what I wanted to do was going to take about 25 years, um, again, wanting to have that engineering overlap along with that medical overlap, you know, a little more intensive than I was prepared to do. Uh, so found my way into the business side, but also the, you know, med tech side as well today, so.

Speaker B: Yeah, exactly. Yeah, go ahead. You were talking about, uh, how you got to be Health Mosaic.

Speaker A: Yeah, yeah, yeah. So going from that angle from biomedical engineering, um, being a developer by trade, um, found my way again into the business practicum side, um, so lucky enough to be armed with an mba, um, at this time and then working towards the doctorate in business as well. And that has given me the exposure and experience now to not only just understand technical acumen, but the business acumen as well. Um, and bringing those two cross functionally together. So intersection between tech, business, healthcare, call it the perfect trifecta if you want to say. Um, and again where I have found myself positioned now, being the CEO and leading m also Health Mosaic and then operationally running as a VP for a medical device company called Fukuda Denshi. So kind of tandem jobs, but they both overlap, um, both with a lot of experience and opportunity in the last decade. Um, having gone from the medical device manufacturing, product development, engineering and then now to the Health Mosaic side using a lot of those key skill sets. So sweet and simple, shortened version. Um, but that's me in a nutshell there.

Speaker B: Okay, fantastic. So Justin, so um, we'll go further into your journey, but before that, uh, you mentioned two companies. So let's start with the Health Mosaic and so you are leading that company. So what does Health Mosaic do?

Speaker A: So Health Mosaic is what we're coining as the first clinical AI marketplace. Um, you know, we've seen a lot of fragmentation in the industry. A lot of people hear the word interoperability which is just connectivity and medical devices being connected. That was my background. That is where I entered into the digital health space and was part of the problem. Right. You have a lot of point to point solutions, one to one connectivity options that are there. And that fragmentation is what we started also seeing in the new AI frontier as well. So still having to band aid and fix medical devices connecting is insane in 2026. But if we only put a band aid on that problem, the AI landscape now is only going to double and if not triple that complexity of having to connect multiple modalities and point to point solutions. So in simplest clinical AI marketplace, that's going to unify this data and empower those outcomes downstream by integrating data systems vertically, um, pulling all of those data sets into a localized area where end users, be it developers for AI can use the data to develop next generation models and or at the same time handle their interoperability issues that are going to occur and have happen at hand.

Speaker B: Okay, so yeah, so I would like to get a little bit uh, further into it because when we use the words interoperability and that ah, stuff may or may not mean much to a lot of people. Okay, so let's take ah, a use case. Um, so first thing is you operate in hospital systems, is that right?

Speaker A: Correct? Yes.

Speaker B: So now let's uh, talk from a patient's perspective. Right. So I go and admit myself in the hospital, then I'm about the surgery or whatever, something is going to happen. And then, you know, the standard equipment that I see, uh, BP monitoring, you know, probably ECG and this kind of stuff. Right. So from that angle, tell me how Health Mosaic operates. Where do you fit in?

Speaker A: Yeah, so we fit in, in the integration points. Right. So when I say vertically integrated, we handle the device connectivity of the modality. Right. So let's take a patient monitor that you're hooked up to.

Speaker B: Okay.

Speaker A: That information has to get from that monitor to the EMR for charting. So we integrate the data there from the edge.

Speaker B: Actually, sorry for clarifying. EMR is electronic medical recording, basically a software that, uh, gets all the patient information. So you're talking about the data that uh, monitoring device has to feed into it along with any charting notes, the doctor or nurse or somebody else. So that piece, you know, that you're trying to address, and then do you guys have a device or is it a software?

Speaker A: So we have both. Right. And this is the area when we say vertical integration. Right. Why that's important. We're handling making that device smart by enabling it with a connectivity hardware component. And then the next piece is being able to have a software that unifies that. So just take a simpler use case and boil it down. You've got a smart light bulb in your house. Mhm. You've got to connect that IoT smart device to the Internet to get data to then to your Alexa. Same concept here. Getting, um, that device, you have medical device, enabling it and making it smart, allowing for it to connect for WI fi and other things to get to the point that information can end up in the electronic medical record. So that one piece of integration is one of the simplest, if not hardest points that a lot of healthcare and hospital facilities have ran into for the last 10 years. The other problem where the Health Mosaic piece falls into it is you've got to normalize or standardize that data. So since we're capturing the data from those primary edge devices, medical devices, we now have the ability to unify it and then normalize it so it can be used to power better AI algorithms and systems. You need good data to make good AI and it's as simple as that. So if we're the core, nucleus, if you want to say, of gathering the information, standardizing, unifying, we can now help, not even on the integration standpoint, but now also for next generation AI applications to be built.

Speaker B: Okay, so if Health Mosaic is not there, okay, so what is happening right now without today addressing the problem? Because the problem is still there. They need good data for good predictions and data analysis and all that stuff. So how are the hospital systems doing now?

Speaker A: Today the only hospital systems that have the ability to do it have data scientists in house or very matured in their journey. So they've got to a find a way to build their own internal tools and build a lot of gather software developers to engineer these solutions and that again, custom build. Right. And that's intensive. Uh, that takes a lot of dollars, a lot of research, a lot of energy and again expertise. So you're not the medical device manufacturer. You've got to go talk to a medical device manufacturer to understand their data, to normalize it. Then you've got to understand the EMR side, electronic medical record side to understand their information. And now you're having to tool and custom build your journey. That could take 18 to 24 months or longer and you're burning dollars and not getting to the endpoint to AI that you could or couldn't use. And it's more of R and D than an actual outcome application for clinical.

Speaker B: Got it. Is it a concept stage or you guys have devices, you have installations. Can you talk a little bit about the stage of openly?

Speaker A: So we've been commercial to market for the last 24 months.

Speaker B: Fantastic.

Speaker A: Um, yeah, we've been lucky enough to be bootstrapped the whole way as a company getting actual customers on board. We have about 25 hospitals across the country and we're working that number upwards, hopefully towards 50 to 100. The goal is by the end of the year in order to use the application and to integrate it. But yeah, our install base is growing and we at least have been able to successfully integrate, standardize this data and we're slowly onboarding AI applications for enterprise onto our marketplace and platform so they can leverage the data in order to build a better and stronger algorithm for clinical outcomes.

Speaker B: Got it. Is there a uh, subspecialization? So you guys operate in like ICU areas? I don't know, different ways you can slice and dice are mid market and not large hospitals. So is there a uh, tweet spot for you guys?

Speaker A: Yeah, today it's been what's called critical access hospitals and rural healthcare. So you're 25 to 75 bed hospitals. They want the exposure that the large universities have for tools and applications. The problem is price to entry is expensive. Like I noted, you need a Data scientist or very sharp engineers, which is difficult to get in these communities. So for us, our sweet spot has been serving that underserved market has been one piece. And then we understand pragmatically the core problem of connectivity. We have supported up to 500 plus different device modalities today. Ventilators, cardiac monitors, infusion pumps, and the list grows. But we're not touting having the largest device library. We're touting understanding the actual problem as one and then being able to actually meet the customers where they're at. And that, that to us has been the most, um, how do I want to say, lucrative as one, but also the, the best approach, right. In order to help understand the customer's needs and those critical access and rural hospitals, because they don't get not even the expertise, but the care, they're left out there, having to figure it out. And that's where we're filling that gap.

Speaker B: That's actually a pretty good sweet spot to start from. Are, uh, you guys horizontal or are you do, uh, for example, you only focus on pediatric, uh, care, you know, geriatric care or any of those areas?

Speaker A: We look at all units, right? So that's the thing, right? That's what I. Interoperability is everywhere, right? You most likely in an ICU or an ER or even down to a neonatal unit, right? You've got a patient monitor, you've got a ventilator. Those things are fairly standard. So again, it's understanding what's the real use, understanding what actually needs to be connected M. And what data is important. So as we take customers through our journey and they go through that, they themselves uncover. We do need to connect this. We don't need to connect this. Or, hey, we have a lot of data here that we could make better decisions on by just having access to our own information. So, yeah, to answer your question, you know, any and all units will cover as long as there's devices in there. That makes sense.

Speaker B: Got it. So I think we addressed the interoperability. So now I have a clear picture. Uh, the audience also, I think, has a good idea. So now you also mentioned AI. So let's talk about AI piece. Okay, so on one end, you could just connect the devices and get the data and, um, make sure that the data goes somewhere like the EMR in this case. Right. So it's dumped there. But are you also doing any analysis? So what kind of AI are you guys really taking care of?

Speaker A: So we're creating our own benchmark models, right. So we're Developing like ECG baseline models for AI to interpret ECG rhythm strips, interpret ventilation data. But here's the thing I'll make clear. We're not in the business of creating and developing AI. We're in the business of being the data providers for the AI solutions. So for us, because we understand again to the core that data, how to label it, how to present it, the AI companies are, uh, who we're starting to work with to present that information to. So let's take for instance, we'll go a consumer one that everyone knows but aura rings, right? Yeah. They need information in order to help empower their algorithms for SBO2 and heart rates. Yeah, that's a provider that needs data from real time medical devices that can connect into our marketplace or into the Health Mosaic platform where they can use real information from hospitals. Right. And it's least that they can leverage in order to develop and scale their algorithms there. So we develop our baselines. Right. To give you a starting point to. But the objective is we want you to be able to build, train and deploy your models with real source of truth clinical data that's validated and verified. And that's the differentiator. Right. Between what they consider, uh, an FDA cleared medical device or FDA cleared algorithm versus a non FDA one. Um, and again, that information provides that for those end user applications.

Speaker B: I got it. Okay, so that means you guys are doing some kind of a pre processing of the data. You know that either you're labeling the data. You mentioned data labeling. I don't know if you guys are doing data labeling, but you're working with some other company that does labeling. Uh, but is there some kind of a processing happening within your.

Speaker A: Yeah, so when we say standardization, that's exactly that. Right. We are pre processing it, we're labeling it and structuring it in a format that you can ingest, um, being the AI application.

Speaker B: Got it. Okay. It's good. I think we, uh, address this one. So we may come back to this later on. So now let's talk about the uh, other medical devices company that you're also navigating, which is a VP of operations there. So let's talk about what you do there. And what does that company do?

Speaker A: Yeah, so. So Fukuda Denshi is known as a cardiac care monitoring company. They're based out of Tokyo, Japan and they have been around for about the last 85 years. Plus their sweet spot in the market has been always around EKG and ECGs. And that is where their foothold in the Japanese Market and the US market has been for that time period. Because of that being on the development side, I've developed the products, the actual cardiac care monitors, the digital interoperability connectivity piece, and then also where they're going next, the AI application set of using this. So this is important, right in the journey of the mosaic and also in tandem with the Fukuda side, because being able to have ECG information is something that's going to power AI models for predictive cardiac measures. So we could look at predicting heart attacks, you could look at predicting post acute moments, um, for patients after surgeries. So again, that's where that tandem overlaps and where we've worked with Vukuda, and we've also been able to work in tandem with them, utilizing our product to help power what they're building as a device oem, but also now to develop and deploy to the market a larger landscape.

Speaker B: Okay, excellent. Hey, um, so now let's, uh, segue a little bit into your journey. At the beginning, you kind of teased us with, uh, your journey where you wanted to be somebody and then you became somebody else kind of thing, by the way. So you're having the best of all the worlds right now. Right. So you're not stuck into one. You have a, you know, healthcare background, you have a business background, and then now you're running a company and then, um, uh, seems, you know, pretty fast paced journey. So there are a set of members in the audience. They do care about the health tech and then the AI piece of it, but they also care about people's, uh, journey, like to become an entrepreneur. Right. So I want to just cover that angle. So how did you start? Where did you start? And uh, then, uh, how did Health Mosaic come into your journey?

Speaker A: It's pretty unconventional right now. I'll be upfront about that. There's a lot of pivots along the way, but that's the story, right? In itself that paints that picture to, you know, how I got started to how I got here, minus the background being from biomedical engineering. Right. And that also helping create the baseline for me. I started two other companies prior to this. Right. One company I started up to this day is probably still my favorite company, and it was a clothing brand.

Speaker B: Now, actually, Justin, before, uh.

Speaker A: Yeah.

Speaker B: So right out of college, you started a company, uh, you work somewhere else, you got the experience.

Speaker A: Let's see. Yeah. Ah, the business side, I started the clothing company while I was in college. Right. So that, that was unique enough for me to say, okay, I want to learn business I like this.

Speaker B: Got it.

Speaker A: And I had that innateness to want to do it. In parallel to that when I finished, right. I did work at a company called L3Harris, um, government based defense contractor as a software developer for two and a half years. While doing that, I started a cybersecurity company, um, in parallel to that. So the clothing company was great. It was a lot of fun. Learned a lot of things. Right. We didn't make a massive profit, but we learned true business practical skills.

Speaker B: Yeah.

Speaker A: Going into the cyber security company, I was a little bit older. I, um, was able to navigate a new space and I was doing my MBA while working full time at Harris, plus starting the security company and all of those overlapped. The MBA I already somewhat had because I was learning how to implement the business in real time. The development skills I was learning from working at Harris full time as well, translated to how to be agile and how to scale operations.

Speaker B: Yeah.

Speaker A: So all three of those the same time though it was extremely chaotic. Right. Gave me a baseline again to say this is how I can pivot. This is how you scale a business. Here's what works and here are the principles to go from there. So each one of those led to those moments and again in the last 10 years. And then after I finished security company, I left that to my other co founder who still runs that today, and was able to be picked up actually by Fukuda and worked with them for the last 10 years in the space of digital health and inside the patient monitoring space.

Speaker B: Okay.

Speaker A: So that, that was where I got that opportunity. Right. And out of seeing the actual pain points and problems in healthcare inside of the medical device space is where Health Mosaic came. Because knowledge industry, the depth of the problem, being able to know the true issues at hand to the deepest of points. Taking that from all the experience prior brought us to where we were now.

Speaker B: So is Health Mosaic a venture of Fukuda Is totally independent.

Speaker A: Totally independent. Totally independent of it. The opportunity presented itself where Fukuda also works with the company now as a customer as well. So we have them and quite a few other device OEMs, um, Medtronic being one of them and a few others out there in the space over time.

Speaker B: Okay, good. So we'll come to the final phase of the discussion which is looking forward. So you have commercialized the Health Mosaics product and so at least for last couple of years, 24 months. So that's pretty good, decent uh, Runway that you guys had there. And um, so which one of uh, the two that uh, takes most of your time. Health Mosaic or Fukuda.

Speaker A: Yeah, it's um, it's going to be the health Mosaic side. Right. I mean it is now the full time investment of what we do and we've been again lucky enough to partner with Vaguda to help drive that innovation and get it forward. But um, that does take most of the time. Right. Myself and my other co founder work full time on it and that is where we're pushing forward, knowing that we have a competitive edge and knowing that we again understand a true problem down to this core where we're positioning ourselves with that.

Speaker B: Okay, so let's talk about the future of um, the innovation, technological, um, innovation that you guys are focusing. So now you have the products out there, the interoperability I believe is addressed. Is it addressed or is evolving? Like you have to keep looking at different kinds of devices that are coming in the marketplace or what kind of innovation or advancements need to happen on the interoperability side of the world.

Speaker A: Yeah, M. So two parts. We are always looking at newest devices. Right. Medical device manufacturers are always making new ones. So we stay at the cutting edge and that's why we have the relationships with them to continue making sure we're up to date. The truth is interoperability will take time to be resolved. Right. It's been a 15 year problem ever since we were trying to integrate what we're trying to get to. The point is any end user in a hospital should be able to get a device, plug it in, connect it and get data to the electronic medical record. If we can accomplish that, we accomplish resolving one of the, again, most elementary problems, but also empower what's going to happen from a data sense of what goes on next. So again, it's, it's, it's iterative, but it's something that we know that we can tackle and we are building towards.

Speaker B: Fantastic. Okay, so that's that side of the world and then, um, on the AI side of the world. So is there an innovation that needs to take place and that you guys are working on?

Speaker A: Uh, from the AI standpoint, right. It is getting customers mature. Right. And making sure that we educate on the utilization of it. Right. It's, there's a lot of trust that has to be around AI, specifically in the health care space.

Speaker B: Okay.

Speaker A: We talk about better outcomes, we talk about helping patients, but if we're providing that data and the AI is now in charge of making decisions, we have to be right 100% as much as we can be. So from the AI standpoint, we can't go out touting outcomes and we can't go out saying things are going to be better if we haven't done the validation. And the only way you get to that point of strength is knowing that the data you have is a source of truth and that it has been clinically validated, tested and refined. So when that has happened, then we know the maturity of the customers will catch up to that point where they start to trust those applications and know it's making either a diagnosis or it's making a call on a percentage of accuracy that then we can move forward and to continue to address the next phases.

Speaker B: Okay. Hey, fantastic. Actually, one thing I forgot to ask. I mean definitely health care is a very highly regulated space. Do you have to work in the regulatory, uh, area? Like some compliance is needed. So anything that you guys need to do?

Speaker A: Yeah, so I mean, obviously HIPAA compliance for the us, GDPR for security and things like that, we are working, um, with the FDA, obviously, um, looking at the Class 2 510k, which is their acronym for a medically graded application. The newer acronym everyone has probably seen is called SAMD Software as a medical device. So that is an area that we do fall in as well as clinical decision support systems, uh, a lot of acronyms, but each one of those. Right. Uh, you have to abide by. Right. It's AI is new innovations and they're trying to develop the guardrails as quick as people are developing. And as a, again as a founder, as a CEO, as being even an AI advocate to developing, you've got to make sure you put those in place beforehand and understand what you're doing and how you're using it. Otherwise, again, diagnosis that are incorrect will have fatal impacts downstream.

Speaker B: You got it.

Speaker A: Um, so, so yeah, absolutely. Doing those things as best as we can.

Speaker B: Hey, what's the big rock that uh, you guys are trying to climb? Right? So I mean from an area perspective, so you, you address some area, the underserved areas and then you picked. And that's where you started out. I don't know, it's larger hospitals and established markets and could be a, uh, challenge. But what is the uh, next big challenge that you guys are trying to tackle?

Speaker A: Yeah, I think the next big challenge is going to be those larger hospitals. Right? It's going to be actually trying to bridge enterprise health systems. Right. Again, the big Kaiser permanentes, NYU's large names, everyone knows, bridging that with what is out there from consumer grade enterprise healthcare systems or devices and being able to find that area where our consumer grade technology is now reporting back to those healthcare systems and we're empowering what we can do with patient data, um, for better outcomes, truly. So it is again, education and it is knowledge exchange now of how this will work and how it gets deployed.

Speaker B: Excellent, Excellent. Hey, Justin, um, thank you very much for your time. So, I mean, we covered a lot of ground.

Speaker A: Thank you.

Speaker B: It doesn't seem like it, but within half an hour, you pretty much can cover a lot. So thanks for your time.

Speaker A: Yeah, no, greatly appreciate the opportunity to be here and present and talk to everyone here, what we're working on.

Speaker B: Thank you. Hey, again, um, to everybody. That's a wrap for today. So unlocking the AI advantage. Ah, episode with Justin Ramsaran, uh, who is the CEO of HealthMosaic AI and also has a second hat as a VP of Operations at the Fukuda. How do you say Fukuda? Denshi.

Speaker A: Fukuda Denji. There you go.

Speaker B: Exactly. So excellent. So with that, uh, that's a wrap for today and then, uh, we'll come back with another episode soon. Thank you, Jeff.

Speaker A: Appreciate the time again. Thank you.