Transcription
Oleg Gradnensky 0:09
Thanks, everybody for joining us today and from those that are from here, apologize for the weather, it looks like it's much better today. Hopefully it will continue. So again, we have a very distinguished group of commercial companies here with us. And we wanted to bring this as my third conference. And we wanted to bring more people, sort of more folks with commercial experience with, you know, as you can see here today and find out later, that was over 20 million of run rate, and kind of talk about their experiences, you know, how they're thinking about the future, what they've been through, and, you know, hopefully, maybe share some some wisdom for folks here that are thinking about, you know, about to go into the commercial stage. So we have, you know, pretty big chunk of medtech that all companies are covering today, so from software to diagnostics to planning, monitoring, imaging to therapy devices. So let me start with introducing Jim from Monteris.
Jim Erickson 1:16
Yeah, thanks. So Jim Erickson. I'm the CFO and Head of Marketing for a company called Monteris Medical. We are a therapy device, doing laser ablation in the brain. So it's minimally invasive brain surgery, using laser and it's MRI guided, and that's where the data part kind of comes in is we use MR data to to visualize the thermometry for on a software package that allows for the the physician to see in real time, what they're doing as of the ablate. So that's, that's monterra.
Oleg Gradnensky 1:52
Great. And we have also three other co founders with us today. So Cameron, with Synaptive Medical,
Cameron Piron 2:02
and thanks so like, so I'm Cameron from Synaptive, I'm co founder and president. We're a Toronto based company. We're about 200 people, we design and manufacture imaging systems, robotic systems, and software that integrates information. And we were really focused on procedural imaging. So the ability to really get the right imaging to support procedures like like Jim's Montero, this laser ablation product, so combination of integrating data, and also creating new data sources where we didn't think what was available today was sufficient.
Oleg Gradnensky 2:38
Great. We have Dave Wang, who's co founder and CTO by IntelliSense, with us today,
David Wang 2:46
everybody? Well, yes, I'm Dave. I'm the co founder and CTO bio IntelliSense quick facts and figures about 240 employees, we acquired two companies last year, what we do is, we basically take essentially all the vital signs and parameters that you'll see in an intensive care unit, shrink it down to a tiny ultra low cost, device or wearable put on a patch. And really we focus in on in facility patient monitoring, democratizing that signal capture, along with automating through AI and stats, models of deterioration. And then also look at both in facility and hospital to home. So, bottom line value prop, pretty simple, visceral pain on labor, nursing shortage, right? So we let patients sleep at night automate all that manual stuff, better outcomes. So you feed in our stuff into the AWS systems and AI alerts, that's a little good. And also, we offer new revenue generation because you've got a whole bunch of CBT goats for RPM RTM, so that not only allows hospitals have a new kind of revenue area, but at the same time, you know, lower readmissions, better outcomes. So that's the quick, quick summary.
Oleg Gradnensky 3:48
Great, we have with us, Brian Greene, who's co founder and CTO of Neuron Sphere.
Brian Greene 3:54
Hi, I'm Brian Greene, co founded neurons fear in fall 2020. With Kevin Yoder, we look at software and data as the core of our business. So we come out of a generation of, you know, building med devices and working in med device companies, and looked at, you know, listening to my fellow panelists and listening to all the other presenters. You know, med devices and software and data are inextricably tied, you know, as we go forward in the future. So we build software to help accelerate that path towards making more intelligent devices.
Oleg Gradnensky 4:26
Great. So, you know, my first question for the panel, and obviously, we haven't any more. So maybe we start with what role did data play in development of your strategic story and business proposition?
Jim Erickson 4:40
Yeah, I mean, I'll I can tell them on Monteris story just a little bit. Ours is probably a little earlier stage and some of the other guys in this panel. So it's an interesting dichotomy, you know, the the Montero story goes back a bit into using energy in the brain to try to use that as a way to Yeah, at minimally invasive brain surgery, RF has been used in the past. But more blind, it was really more of a measured energy delivered what what tissue should die. And part of the vision was can we can we use something to try to visualize in real time what's happening while you're using heat to kill tissue. And that's where thermometry came into play. And so some of the vision of the early founders of our company was trying to take this MRI data, a patient in MRI, and take this information, which is really phase change data of, you know, the patient's essentially activity of water molecules and turn that into a visual representation of heat. And so that's part of the software that we, that we provide, it's really part of the game change to this technology being legitimate in the brain and in in neurosurgery. And if you if you can't see what you're doing, obviously, there's a lot of collateral damage you can do. And so that integration of data became a real big sort of building block for how we then can deliver that laser energy and actually start to provide a therapy that can be become part of a, an actual care paradigm. So I mean, it, it was that that sort of led Monteros into the data data part, we have a software component to what we sell, that then becomes part of the ongoing service revenue stream. But you know, part of our story is also in the traditional, I would, I would say, clinical data, it was a 510 K device, that didn't create a lot of what I would call real outcomes based clinical data to get on the market. And what we realized when we, in the, in the earlier days in the mid teens is we've really got to get to that if you're going to try to change market dynamics. This is not new in medtech, which is you've got to get to really good disease based outcomes, clinical data. And so we took the traditional route, we've got a post market registry, traditional in the sense that it's sort of a clinical structure. This is now where I start thinking about things with how do we get better at we've got all this good performance data, and how do we marry it up with what I will call traditional clinical data. That's something that that that both have been important to our story, kind of the integration of Mr. data, a lot of what things like these guys are going to talk about in the software space. But then traditional clinical data becomes a big part of how do you, you know, disrupt markets and actually create a market for building a device that can deliver therapy and add value into the into the providers care. So, you know, it's our story has been a little bit of this computer science, software data, but also traditional clinical data. And I think that'll be an interesting part of what we maybe talk about here, throughout the panel.
Cameron Piron 7:52
Great. Yeah, our whole, our whole reason for being is really, the generation of new data where we're people's not, you know, it's either in sufficient or inaccessible, or the integration of data that's available out there today. So we have staggered products, one, one of them, we launched two years into the company being launched, the other took eight years, that was our MRI system, we're really motivated by our surgical product line, which is our first set of products to do what we found is really exceptional practice at a few Institute institutions, such as the integration of MRI data, in the ability to create connectivity maps of the brain. So your brain is give or take about 90 billion neurons. And MRI is very good at imaging water and good at imaging the movement of water within the brain. And you can actually trace it trace out all these connections. And in exceptional practice, you use that as part of neurosurgery or decision making for implantation of things like deep brain electrode. So that first step was taking data that was available out there, creating new data from it, and presenting it in an automated way to clinicians. We then layered in different levels of sophistication, the ability to take each of those fibers and, and characterize them based on their function of the brain. So we could take this complex data and reduce it for clinicians. And then then actually creating for what we saw huge problem in the operating room was integration of other information, optical information to align with that information. So we actually create our own robotic optics system that created better higher definition information that could be integrated with that Mr. Information, so that we call our surgical product line. And in the background, we forge forward to create our own MRI system. And it's a small footprint high performance system that has very low distortion and really uniquely quantitative data, quantifiable data that you could serially track with patience. So we've taken a very long vision with this company. I think at the beginning, we were very happy with that. And partway through we thought maybe we made a mistake and now here we are on the other side of it with all the products launched and, of course, genius moving in retrospect. So
David Wang 10:05
yeah, no, I think from a biocide data is the fundamental organizing principle, right? When we think through the business model the strategy, and I'm gonna make this analogy. It's not perfect. But when Jim and I started the company, we're like, Look, you got this thing called the iPhone, right? It's essentially a phenomenal signal capture device. And yes, first party titles like iTunes and apps and photos and GPS. were obvious, right? But then you saw this huge long tail of applications from third party developers. I was like, 800,000 apps, right? So when we think through like when we founded the company in 2018, right world record, first FDA clearance on the first device in 18 months, right, starting into hospitals, when Whoa, we are seeing so much data and really looking at against non consumption, right. Our markets were med surg, where it's a manual once every six hours, a nurse goes in for one minute of data, and then a home worth blank, right? So so essentially, we're saying, how do we take all the vital signs and data from the ICU, democratize it and just spread it everywhere. So we don't sell hardware, everything is a catalog of services per day per month, depending on the use case. The second insight was by just saying, wait a minute, we got all this physiological data, but how do we actually close the loop on outcomes? And how do we start, you know, fusing data, not just from the body, but from the labs, from the medication history, demographics, and all that kind of stuff. So we acquired a company called alert watch, last year, over 1 million surgeries, ingestion of EMR all that data, right. And then we started to scale into our bioburden platform saying, Well, this is super interesting, because now, if I can look at your heart rate, blood, oxygen, blood pressure, to core body temperature, ECG, blah, blah, blah, blah, blah, and Emerg, that with your lab results, medication history, all these pieces, you start having this huge opportunity to automate through software, all these very complex manual things that are both error prone tons of false positives, and just automate that whole piece both in the hospital and at home. And I think like, as we look at the next phase, it's a lot like, you know, everyone's talking about chatGPT, and, you know, LLM and large language models, but it's like, phase one is like, how do you go from atoms into bits and start cranking like the standard stuff, right? So if you look at chatGPT are barred or whatever, they're gonna all start converging to very similar types of problems and similar us right, you start scraping all the physiological data, all the EMR data, you'll start seeing all these low hanging fruit, that's gonna be obvious. But the next phase is what are fundamentally unique signals, you can bring in new ingredients are training these models, and start creating new clinical applications. And then can we create an ecosystem where we share this data? And for different use cases, different types of data will be really interesting. So that's the high level thoughts on data. Great, thank you.
Brian Greene 12:35
Yeah, I guess they take a little bit different approach in that we, we, you know, I spent my career working with devices, you know, kind of underneath this, and looking at where are we using the data for what are we using the data for? And, and really, it's hard med device. And technology are interesting, because on one hand, we see this amazing technological innovation. On the other hand, as a buyer of software, and a med device company, you go try to buy something, and you've talked to your vendor, and then you say, Oh, well, we make a med device. Tell me what you know about HIPAA. Right? We're gonna go into Europe, tell me what you know, about GDPR. Tell me what you know about data sovereignty, tell me, you know, you know about validation and control. And it's a lot of really boring subjects, right. Like, if you want to have some fun, and you need a nap, I'll take you outside, we'll talk a lot about software validation. But as an industry, med tech is behind, right, like from our perception, because a lot of the vendors are selling non validatable solutions. They're selling things that are fine if you're making advertisements and videos of your dog, but they're not fine. If you're trying to make videos that save people's lives, right? You're trying to aim a laser inside somebody's brain. It turns out, we want controls. And so when we looked at what are all these companies trying to get to as buyers, buying software that had all this boring stuff built into it, so that you could get to the fun stuff. That was our real underlying motivation, right is is it's too hard to do this, right? I love working with people like this and talking to people, you know, I'm gonna take this problem, I'm gonna go get it. But how do I get more and more startups? How do we get every company who's presented here that says, Oh, I've got access to a new kind of data asset? How do I turn that into new clinical value? And so our first principle was, that's what we think most companies are doing in med tech. Let's go Let's go solve that problem.
Oleg Gradnensky 14:29
Great. I think you know, it's funny, because sometimes when you sort of remember when, you know, we start talking about data a couple years ago, people were like, Okay, that's kind of interesting. Now, it's almost, you know, if you try to go to a hospital and say, Hey, can I can I take your data? Can I share your data? The answer was like, No. Today, it's a very different situation. I think everyone understands that you need to collaborate, you need to find solutions. I think COVID was a motivator for everyone to really focus on the end goal the page Trent, I think that today, it's a huge market. And we're not even near the beginning of it. And I think collaboration is probably one of the things that everyone is talking about today, between the payer provider and the patient at the same time. So, you know, I guess my next question would be, how do you guys see sort of the collaboration and you pulling so much information? I mean, from imaging to diagnostics to therapy, is it sort of, you know, you hear kind of big companies talking about, we want to get data from, you know, EMR to sort of preoperative operative post operative recovery, let's get all these data points together, you know, let's just for the fun of it, let's just add genome on top of it. And let's figure this out, right. I mean, it all sounds kind of futuristic and Star Trek II at the end of the day, but then actually, we're in healthcare Atlas, just step back here, you actually talking about people's lives, right? You can just put a FinTech solution on the human and, and, you know, swing it right. So the repercussions are huge. So how can we, you know, collaborate around sort of this multi dimensional sources? Towards improvements? I mean, how do you guys see that going forward?
Brian Greene 16:21
I mean, I guess I'll start with this, when I, you'd love to talk about just simple data standardization. So we saw a lot of effort around clinical, you know, basic standardization, so that we could get payers paid, and we could do insurance, and we don't get fired. But then when you look at devices, there's zero standardization, either, you know, the data that you produce is different than the data and I, for us, it was how do I start to produce a new layer of standardization to bring this device performance data up to the same level, like I can make my EHRs talk to each other. I can make my EHR, talk to insurance companies. What I can't do is make devices that are producing really valuable information, the layers between there and the EHR, there is no standard. And so for us it is how do I start to build those standards? How do I build those standard tools, at a more theoretical level at a more maybe feel good level? I like what you're talking about with an ecosystem. And I think that the device manufacturers should be trying harder to share their data. Right? I know, it's counterintuitive, it's trying harder to give it away, trying harder to provide open access to people who want to study it, learn from it, because I think there are innumerable products and numerable patient benefits that we can produce. And I don't think any one company will ever create enough, you know, have enough resources, enough data scientists or whatever, to to fully realize the asset. So I think we, when we start seeing companies sharing more on common frameworks or device data, that's my vision, right, we'll see a different leap. Similar to what we saw with clinical data.
Jim Erickson 17:56
I'll echo that. Brian went a little bit where I was, is, I think, you know, it's a therapeutic device that started using data from MRI imaging machines. You know, our technology started long enough ago, the story goes back aways, that nobody was talking about data and how it drives everything. But that's what we're doing is using MRI data to try to visualize something. And even today, you know, trying to have a good enough relationship with EMR vendors to tell us when they're gonna update a software package. And now we got update, and patch software to make sure that the way it's at Brian's point, it's got to be validated, I have to, I have to know and understand that what I'm getting still processes the same way it did before. And so there is a little bit of I think, you know, Cam and his company in ours have talked before, there's got to be a little bit of this open dialogue of you've got to park a place in the value stream, we've got to place in the value stream, the fact that we're going to acknowledge that together, share the information to make us both better is to me not given up any value your own company, it's acknowledging the end to end, you know, care continuum, and saying, We all hold, play a place there, the more we know about each other's spot there and standardize that data and share that data. That's when technology is going to really start to get good. And we don't just talk about, you know, in an individual companies, what data did my tech create, and what are we going to do with it, but what could it be done for if this other tech down though, you know, down the works, or the value stream? Got it, whether it be diagnostics, feeding to therapy, or vice versa? So I mean, I completely agree with Brian on the trying to find some more standardization because we all know it can drive value. We all know it can provide intuitive research development, but you got to sort of see that incentive for yourself.
Cameron Piron 19:52
Yeah, that thing is oh, great points. I think in the imaging world, it really took off when Dicom, conformity came out. So it became a standard that everyone had to follow. And, you know, I think in my prior prior life, I made equipment for the big MR vendors, and we made equipment for breast screening. And if you look at particular subset of imaging, you look at breast screening. It's not just DICOM conformance, but it's also quality and quality system. So you have to qualify the screening center, you need regular checks of the technology in in because it is such a high bar of early detection and disease, everything is firmed up really well. So I think the rest of the industry needs to kind of firm up along that pathway. You know, it's interesting, because one of the reasons we made an MRI system is we saw that the current systems were only made for radiology not made for procedures. But another aspect is we really thought we wanted to open up the architecture. And because I'd say it's fair to say these systems become more complex, bigger, and become a little more closed. And I'm, I'm an engineer from Waterloo, Waterloo is known for a little device called the Blackberry. And there's a really well understood story there, you know, there was a first in in world device, and their thesis was absolutely bang on, we will all have these devices in our pocket. But I don't know if there's anybody in the room could show me a Blackberry, you know, device right now. They're all they're all, you know, other vendors, they had a very closed architecture, and very, very difficult for people to innovate on their architecture, where others came in with a very open architecture, and boom, took off and the whole field grew. So I think there's a lot to learn. I think those are great points.
David Wang 21:36
Yeah, I think just echoing the other panelists, I think when I look at the data landscape, right, bio is in a very little myopic niche. But we're very lucky, because if I think through data friction, right, one thing that's been amazing since day, one of the very first agreement we signed with every single hospital now is that we basically get the de-identified data. And then with the acquisition, we get all the EMR data, that's kind of part one. Part two, from a regulatory side, with the kind of like guidance of Sam does write software as medical devices, again, in the little vital sign world, we're very lucky, because the way you do heart rate, respiratory rate, IE ratio waveforms of ECG, they're pretty darn standardized. And how those outputs feed out are all based on predicate validation. So we're pretty lucky there as well. And so what we're trying to do here is basically reduce friction from an ecosystem perspective was called Time to revenue. Just like Apple spent a lot of time getting everyone's credit card, doing all the privacy for consumers and hosting the whole app store and everything like that, we're doing something very similar. Where if you are a innovative startup, who doesn't want to go through an FDA clearance of devices doesn't want to go and sign each of these data rights with hospitals, good luck with that, right? And doesn't want to worry about security, and HIPAA, and high trust and all those kind of things. But you want to just really focus on your sweet spot and hit revenue fast. The hope there from our little ecosystem, is to allow that and to really open that up, so everyone can kind of like, yeah, find that value.
Oleg Gradnensky 22:53
Great. I think, you know, one of the things that I don't particularly feel like, I'm smart enough to talk about sort of data monetization, and you know, I just feel like we're, you know, ages away from that, frankly, I think right now, it's more sort of capturing your ecosystem, capturing your real estate, creating the infrastructure, and then sort of see how all these different systems interact with each other and what that actually means for the patient or the provider. And I think, you know, we're just at the beginning of this whole process. So. And I guess one of the misconceptions that, you know, and I wanted to, you know, we will want it this session to be practical, and kind of talk about things we can do today. And, you know, particularly having folks that actually have, you know, commercial revenue, customers and reliance sort of on their product line. I mean, what are the, you know, some of the misconceptions about software, indeed, are in sort of into Moore's medical devices, how can we sort of alleviate some of that and kind of be stay focused and delivering things that actually need it today? versus, you know, Star Trek?
David Wang 24:02
I can take a quick stab at that one. So when we look at product market fit, right, the the main thing is like, where's the visceral acute pain, right? You don't want to sell vitamins, you want to sell painkillers. And so what we quickly realized during COVID Was that nurse labor shortage, and these things were very acute pains. And so in terms of commercial traction, we hit this inflection point, when the little light bulb went off saying it's like Geoffrey Moore's Crossing the Chasm, right? If y'all read that book, it's like, when you want to disrupt, you have to do a whole product, not like a little piece, here's little algorithm, here's a little widget and you gotta go from the workflow reimbursement on all those pieces all backwards, and create that thing, we just kind of drag and drop. And so once we acquired a couple of companies and built that kind of cookie cutter, just drag and drop, then that kind of sales cycle just started to compress dramatically. And then once you standardize along that piece, right, the big lightbulb was we have to own the clinical dashboard and that whole workflow piece once we acquired that company, it started to flow super fast. And then the benefit of that was like well with that dashboard, and that whole EMR integration ingestion. Wow, all the tie backs to outcomes and all these problems and ICU transfers if they were all part of that, which then led us to feed back into the value proposition with automated reports out. Right. And then that was kind of like the critical piece that I wish we kind of thought of a little bit earlier.
Cameron Piron 25:16
Yeah, the great points, I think, when one thing I think, is kind of a challenging concept, but maybe, you know, all data everywhere, all at once all the time is not like every that is that's not the panacea, right? There is costs of data, there's risk with data, I think, increasingly, you're seeing more and more cyber hacking. And, you know, I think I saw a stat not too long ago that healthcare record information is more valuable on on the dark web than credit card information, right. So as we start aggregating these huge datasets, it becomes this, this, you know, treasure, but there's a risk with that as well. So I think that you know, this balance, so making sure you have really good data in appropriately captured at the right time and not drown in it.
Jim Erickson 26:05
The only one thing I'd add maybe is, Dave kind of touched on this a little bit, but, you know, traditional clinical data, clinical setting type data, it's not going to I, in my opinion, is not going away, that's still part of the data that really drives medtech, there's, I mean, I've been here now for a day and a half. And there's just there's no shortage of companies and great technologies going after, really, you know, performance data, and all kinds of different information system type data, at the end of the day, you know, controlled clinical trial controlled clinical data with outcomes, is what really drives payers, it drives provider care, it drives decision making, what I wish we'd have thought about early stage is kind of that as a data set, as something you could connect to the performance, our system talks, you know, creates a lot of recorded data on its own performance. And then we've got this what I will call more traditional approach to, you know, IRB run clinical registry that we have a bunch of physicians putting data into, and then we'll talk to each other. And we, you know, we have to really think about how do we jam that together and learn from it. That's the piece, I think everybody's ready to talk about giant datasets. And I still think we can't, as an industry forget that we have to connect this back to traditional clinical outcomes, that will still matter, I think, to physicians, you know, and decision making.
Brian Greene 27:39
I think the biggest misconception I've run into is that, and this is it ties into what you talked about, the more data you get, the more opportunity there is for bad things to happen. You're right, healthcare records are the most valuable records if you steal them. On the other hand, I think there's a misconception around hospitals, clinicians and even individuals willingness to share their data. Right. I've talked to a startup founder, she says, Look, I was having medical issues a few years ago, and I would tell anybody, anything about what was going on with me. If anybody could help me, I don't want you to do something nefarious with my data. So I think there's a willingness, I have Crohn's disease, I have a chronic illness. I'll share that with all of you. If anybody knows how to make it better, that'd be great. I think people are happy to share their data, if you don't do something nefarious with it. And so the boundary and the requirement for being good citizens, right with medtech, I think if we can continue to raise that people are happy to share hospitals are happy to share hospitals are building data centers, hospitals aren't shared scared a cloud, the new generation of doctors grew up with iPhones, right? There's this whole perception of people are scared to share it. I think it's shifting, as long as we can not do bad things with it when we share. So I think there's a twist there.
Oleg Gradnensky 29:06
And I think one of the things I mean, I guess I agree with you, I mean, it's it's almost, you know, it's very critical to make sure data is protected, obviously, you know, I think all the other industry should learn from health care, and our lawmakers how to create a model that will protect other data. I mean, thank God in our industry, it's already protected. We don't have to go through that. So one of the things I think is also important, just kind of staying back on point of practicality. I mean, how how do you think the and I know that the, you know, Dave, you guys are on top of that, particularly when you're thinking, you know, how I could, you know, integrate sort of what's the proposition to the payer? You know, if you were just sort of what's my commercial sort of offering, right? How do I push my product? How do I make that decision? Easy. I mean, if you guys obviously have to go through hospital approvement for capital equipment purchases, you know, it's like lifecycle nine months to You know, for your accounts probably even longer. So I think, you know, how can you use information? How can you use data to make a case that this whole process is actually faster? Because at the end of the day, if there's a solution that's profitable for the hospital, and they're sort of stuck in their communities for 12 months, you know, they're actually losing money. Right? So how do you make this work better, faster, more efficient? What do you need to do?
David Wang 30:24
Yeah, I'll dive in. For our experience. I think the first things first is like, you start with a, you know, MSA, right, you go, Hey, here's the value prop PowerPoint slides, our CEO, their CEO, handshaking, high fiving is really cool. But in the first phase is basically we call it we kind of gotten this to be cookie cutter, right? You have a three week to fork window, you do what's called, you know, observational, so stick the buttons on everything, don't change any workflow, just do what you're doing today. And we just log all of our data, and we do a retrospective afterwards. And we look at every ICU transfer every bad outcome that happened, right? What could we have predicted? And that's just like a repeatable, oh, wow, that's awesome. Step two is cool. Now you start moving to phase two, where you start changing workflow, right? Let patients sleep at night, let nurses do other stuff, right, discharged a little bit earlier, reduce that length of stay. And then we have general metrics. But then for each of those goals, we report back that you know, to the stakeholders, and that's extremely quantitative. And really important. And part of that upfront agreement is to get that data back from the provider to make sure we can close that loop very consistently, very quickly. And the last point I'll make on this whole data monetization pie in the sky, we take a very practical kind of crawl, walk, run. So it's kind of like like an iPhone, right? We have our app, which is like on iTunes or mail, right? You got the monitoring the basic stuff, sepsis or whatever, then it's like, hey, let's start looking at another algorithm like, you know, orthopedic rehabilitation, or body rotation. So we very specifically look with lead partners understand the money in the flow, and how all the stakeholders think through it, and then we price it, and then we check it. And once that actually starts to see traction, then we start to do general availability. But everything is a very specific thing versus this constellation of possibilities that that completely D focuses your team. So we have this kind of like grooming process, right? Here's a set of 17 things we could do run through the diligence and say, Okay, our next chess move is this one clinical use case, these are the folks are gonna pay us next quarter for it. And the other nice thing is once you get the plumbing and the infrastructure and all things going, then just tacking on like a catalog of services that they can opt into. That's been pretty cool.
Oleg Gradnensky 32:19
And now do you guys sort of think about this concept where essentially, there's a convergence? It's almost where med med device industry becomes the software industry in a way where it's almost I mean, you have to have a device to get there. Obviously, if you don't have one, that's it's very different ballgame. Unless you're Brian. So. But, you know, I think otherwise, I mean, how do you sort of see this convergence and sort of I mean, every single investor would tell you, I want recurring revenue, I want high margin. You know, obviously, in this environment, it's extremely difficult to raise capital, and correlation with public markets. I actually overheard someone telling me that they're zero pre money term sheets floating out there. You know, so But besides the point, I think one of the things I want to rebuttal time left to talk about sort of, you know, how important it is today, you know, in your fundraising process, availability of sort of data and sort of software infrastructure in your businesses? How much kind of, you know, smart investors that you're talking to, are asking you these questions about, you know, how are you thinking about data? How are you thinking about the monetization of your data? And, you know, obviously, we have, you know, veterans here to have been through many different startups and raise a lot of capital. So I think it'd be great to share your wisdom with with everybody in this room.
Cameron Piron 33:46
Maybe maybe, from my perspective, soft software has been very focusing. So, you know, we have a bunch of dirty, dirty words and accompany like, big MRI equipment and robots, capital equipment. So investors, of course, you know, recoil initially. And then, you know, when they see that it's helping people, and there's meaningful length of stay improvements, and that it's hitting the big line items that affect hospitals, that that changes the dialogue, certainly, but a layer of software that really aggregates that all together is really compelling. So, for instance, we use our our magnet for stroke detection, or MR system, and people would look at the data and go, Wow, that's compelling. You could see the stroke, but you automate that you showed an immediate rapport, and then it just galvanizes the whole value equation and chain. So it's like 99% of the work was building the MR, that 1% was the analytical software at the end that brought it all together into a meaningful conclusion. And it just was transformative. So we kind of did hardware for software after and yeah, it goes from kind of glazed looks to intense stares, I guess
Jim Erickson 35:01
Yeah, I mean, we did a raise in September. And you know, ours was a little different to where we are a typical razor razor blade model, the old school piece of capital with disposable components to get consumed, but there is a software component to it as well. And we build that into our service revenue stream. And so it's valuable, it's valuable in that it's your right. To Cam's point investors kind of start with capital, yikes. What's recurring, and you know, so that helps I think about it. Also, though, as another thing, you can more easily as the wrong word, but more consistently iterate on, you know, software is something you can improve in a controlled way. Without, you know, adding benefits really get to user interface more often and kind of create that that r&d cycle. So I think it helps there. The one the one last piece I'll mention from an investor's perspective, though, and it's a little tied into your other question, is it still though everyone talks about this business model and recurring revenue, it has, to me it still has to fit in the reimbursement model at the end of the day providers, they're like sheep, they follow what CMS and payers tell them, they will pay them for, right. And so whatever model you want to come up with, it's a little bit of I keep coming back to you got to, you got to find a way to for that to fit that financial model to fit within the Finance, Financial model of the provider care.
David Wang 36:31
Yeah, I think like, from our perspective, when we raise capital, it's a pretty naive assumption. It's like, look, you know, we're an entity, an asset that supposed to generate cash flow, right. And I think that one of the really important things are widgets are dirt cheap to make. And so we go to hospitals, like it's like, Look, you have all this amazing value prop, the cost is less than a gauze pad, right per day, it's like nothing. And so if you can drop that upfront, capex way low, and then you can basically start showing that stickiness, I think, from an overall SaaS models really retention and an overall kind of like engagement monetization with each socket over time does that accrue with more and more and more services? And are they just like, once they're in they're in? And we're just seeing those very strong signal across the board there. But yeah, I think all of our competitors were like, Hey, we're Harper companies, here's a $300 widget, it doesn't fall into reimbursement that isn't, you know, so we just again, across the board, it's adoption, friction, regulatory friction, just keep the friction super low. And keep your eye on that dirty asset of data. That's just not for fun. But to close the loop on the value prop, right? You can't just have open data.
Brian Greene 37:30
Okay, I'm just gonna make my joke. I think software must be critical, because it's where we fix things that hardware couldn't do. Right? Isn't that what the hardware guys say? Oh, we couldn't get it done this read fix it in the software. So every company has to be a software company. Really, you know, your your car at this point is a complicated piece of software. And it won't be long before there's a recurring revenue charged to it. I, I went to a summit the other day, and I talked to a principal software engineer who works for a garage door opener company. And they are looking at how do they do subscription models. And it's working like there is a reoccurring value. I was blown away by this. It's beyond inevitable that, you know, given the amount of software that we have in my devices, given the amount of data that we're bringing in, and the App Store analogy, I'm in half a dozen people right like everybody's like, we're gonna go do this. It is inevitable that hardware companies are going to be a software companies as well. I think the reimbursement is interesting. It's going to catch up. But you can also see with all of the codes that are out there, that you can do it with software, right like that's well recognized now.
Oleg Gradnensky 38:36
We're getting kicked out so thank you guys for for joining. Thanks to you. I love what you're doing and please thank the panelists with me there.
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