Video Transcription
Justin Ramsaran 00:00
Good morning, everybody. My name is Justin Ramsaran, and I'm with R Group. So R Group, who are we?
Justin Ramsaran 00:07
We're a group of innovators that came together in the last two to three years from different fields: med tech, software, finance, and other applications from AI, in order to start developing next-generation data AI systems to empower clinical insights for the future. We look at the current market space that we operate in. You have OEM medical device vendors and hospital healthcare systems—large markets, right? We've got $6.2 billion in interoperability, $3.4 billion in the IHE space, and $1.7 billion in the med tech space for devices alone. The craziest part of this is each one of these has been fragmented solution sectors for the last decade or more. When we look at connectivity challenges, over 15 million devices today are used across the U.S. hospital landscape—2 million different unique devices. Each one of them requires manual, tedious labor. This task introduces more inefficiencies and also reduces the actual operational efficiency within healthcare systems today. What's the real issue, and how do you resolve this problem? These medical devices, out of the gate, were created for their actual utility and not connectivity. That is the actual issue at hand: a true comprehensive insight and a way to actually connect these devices agnostically and also incorporate those data sets for AI is truly the next generation and how we deal with this ongoing issue. So what happens with an unconnected device? $6 million a year is what you look at in overall annual loss. This comes down to operational inefficiencies, medical errors, adding things in the EMR incorrectly, and then also just in compliance and revenue standpoints. Optimization is key. The area is primed for automation as well, but also needing to increase the actual overall ROI inside healthcare systems just around connected care. How do we solve it? Comprehensive data connectivity—an agnostic way to really and truly combine the efforts of data and bring that to the next forefront. So the ideology we've built at R Group has been around product, platform, and ecosystem. So how do we get there? An ecosystem in itself has the connected capabilities of every one of these devices from each one of our products into a platform. Going from left to right, we've got our analog device, which allows and inherently takes the unconnected device and connects that. Think of it like a smart IoT device, making your medical device applicable. Open Connect became our agnostic platform for EMR connectivity. Think of it as the Switzerland where all these devices can connect agnostically and send data to the EMR—images, discrete data, waveforms, and so on and so forth. Built into that becomes the actual optimization standpoint, with an actual management system built into it that allows for BioMed clinicians to know if these devices are being utilized, if they've been serviced, when the last time they were managed, and so on and so forth. Going from that bottom-up approach really increases the overall capability and the ROI for the healthcare system. With that basic alone, our newest and latest, greatest concept around Health Mosaic—now this is what we consider our future state or forefront concept and idea—by handling multi-modality AI systems with us capturing data at the edge point from the actual medical devices themselves. We have data that's not diluted. We have data that hasn't gone to the EMR, has errors and issues, and needs to be refined. Capturing it at the actual edge allows us to start performing structured data sets to implement downstream into higher application systems. From there, we look at our LLM model, where we've got GPTs built in. Ask it a question, and it gives you back information. Things like that start to inherently build these processes and add the actual AI concepts for the future, which structure data. So our analog device, as you can see here, essentially has the capabilities for plug-and-play technology. You name the device; we've got the library built for the drivers inherently built, and if we don't have it, we'll build it. That's the agility that we have built into the actual operation suite of this actual device and our patented auto-sensing technology as well. The Open Connect platform, as noted, again, is a low-code, no-code platform that provides the opportunity for any operational IT team or BioMed team to connect this information in and do it themselves. I've been on the medical device side. I've built devices. I've been in the integration standpoint, and I hated getting the calls as a sales engineer all the time, having to handle these issues. We give the hospital the opportunity to handle their own modalities and actually connect what's needed from a clinical standpoint. There should be a quick demo here, just a quick 30 seconds showing you how quick and easy it is to actually add this device. In about 30 to 40 seconds, any clinician could go in here, any IT BioMed, pick, choose the device, configure it where it needs to go, and right from there, you've got discrete data. It could be HL7, could be other insights for waveforms and data that goes towards the EMR or any other data system you'd like. So this is, again, the intuitiveness of the low-code platform, being able to test and show that actual capability from start to finish. Just like that, the device has been added and incorporated into a platform. Now you've got content management of where that's at and being able to locate that asset in-house. This has been truly a fun side project for myself and the company as a whole, but creating a source of truth where other systems and other clinicians, BioMed can go in and gain insight. You have to read the manuals for these things. There's a lot of insights you have to go through, a lot of intricacies to know how these things work. As we've gone through and we've done the development, we're doing the hard work here and giving you an easy way to access that information with this LLM-based model that we have. It's got information from all the device manufacturers, all of the information on how to put configurations, settings, modes, even down for clinicians to actually understand the true output of the objective information they can gather. And again, coming back to something we've continued to establish and build for our next development model has been this whole Health Mosaic, multi-modality data AI set. This is to empower the future algorithms for AI for medical devices, empowering clinicians and other healthcare systems to start being their own chief data scientist without even knowing it. That information that's refined gives insight, gives prospectus, and allows you to build and share and lease that information back, looking at market trends and demands. Again, we've seen the word AI come up, but you can't have AI without proper data: garbage in, garbage out. You need to have a way to construct that information and be able to utilize it to its fullest, and that's what we've been able to do here with the basic platform of our Health Mosaic and information system, continuing to operationalize the change throughout the dynamic is how we've been able to progress as a company. We are fully bootstrapped today. We are cash flow positive, and we've been in business for about two and a half years. So, you know, we've worked hard to maintain that and continue to progress, looking towards funding here in the future, being able to increase our overall pipeline. The approach that we take—operationalizing that change, creating strategy, and then being able to innovate faster and quicker—has been the main approach. Customer partnerships we have today include large enterprises from the med device standpoint, from Fukuda, Medtronic, Epic, and even Hamilton Ventilators, and our SMBs, such as Defy Health, Marsden Bio, and then also Data Scripting Solutions, which has helped us advance our portfolio and our platform. Large market wins we've had, as noted here, range from the NHS to large pilots that we are conducting right now, and then, obviously, live with the University of Alabama with our Mosaic platform for real-time monitoring, but also for data connectivity and acquisition systems. Competition is ripe. There are a lot of competitors in the space that we're aware of, but the main differentiator is the way that we're able to connect, the ease of use, and the ability to deploy and aggregate those data systems. The competitive advantages we bring to the market landscape are clear: being cloud-based, being agnostic, having an open connectivity platform, and intuitively allowing the end users the capabilities to connect themselves has been the main proactive winning factor for us. Our team is constructed of a series of past X Microsoft employees, Qualcomm employees, and developers. This is how we've been able to scale and grow our company in an agile approach at the same time. Looking at the product roadmap, we have optimized systems in their next routes, enhancing the operations and looking to increase connectivity in order to keep driving the actual fraction forward. So being able to continue down that route is what gives us that time and gives us that functionality to expand growth stages as we continue to go through. This is where we're looking to expand that time period and drive our customer-like growth innovation down this stand and down this line. Locations and our current customers, again, syndicates, continuing to expand and continuing to grow is the way that we're looking to continue driving our actual innovation downstream. So.
