and get started, everyone. Hopefully more folks will join us as we continue the conversation. So welcome, my name is Jacqueline Shrabati, and I'll be your moderator today for this session, The Innovation Last Mile, The Journey to Digital Health Implementation. I'm a practicing general cardiologist, and I'm also a digital health enthusiast, having built several consumer and enterprise health experiences at Google, and prior to that, a live core. So well over for many years, well past a decade, we've seen a wave of point solutions in digital health, and they've been backed by venture capital. But all of us here know that that landscape is rapidly shifting. The implementation playbook is being rewritten for digital health, in part driven by two powerful forces at play, the rapid acceleration of generative AI, as well as an economic pressure to have more value-based care models. So for this session today, we have a pretty diverse panel, and we're gonna talk about how the technological advancements and economic pressures at hand are shifting the way that companies and healthcare systems are designing, launching, and scaling digital health solutions. We have a diverse panel with a variety of perspectives, from academia, from industry, from government, and we hope that by the end of this session, we have a more comprehensive understanding of the opportunities and risks at play. So I'm going to introduce the panel so that we can jump into the conversation, so let me bring my notes here for that. We will start with, on the far left, Dr. Peter Noseworthy. He's a professor of medicine, the chair of the Division of Heart Rhythm Services, and medical director of business development at Mayo Clinic. In addition to his clinical practice in cardiac EP and his research activities, Peter oversees business partnerships and strategic collaborations, equity investing, and the commercialization of Mayo Clinic technologies. Next up, Dr. Mike Ennin. He's a leader in McKinsey's medical technology sector and the global head of the MedTech R&D practice, having spent nearly 15 years in operating roles at innovation-driven cardiovascular device companies, including peripheral arterial and ventricular assist devices. Mike works across life sciences broadly with special interest in growth, innovation, and commercial performance. Next up, Dr. James Colbert. He's the chief medical officer of Memora Health, an AI-powered platform that helps manage complex care needs and streamline clinical workflows. Prior to Memora, he worked at Blue Cross Blue Shield in delivery system innovation and analytics. James is a practicing internist and has expertise in value-based care, population health management, and healthcare informatics. Dr. Renee Arnold is currently entrepreneur-in-residence with both BioHealth Innovation and the NIH NHLBI, with expertise in health economics and outcomes research and evidence development related to reimbursement. Renee is president and CEO of Arnold Consultancy and Technology, where she oversees outcome research and develops affiliated software for pharmaceutical and government programs. Venk Varadhan is the co-founder and CEO of NanoWear, a healthcare-at-home remote diagnostics platform utilizing proprietary wearable cloth nanotechnology, and FDA-cleared AI algorithms for cardiopulmonary assessments. Previously, Venk was an investment banker following marketing and sales roles at Eli Livy and Sanofi. And Dr. Rod Passman is a cardiac electrophysiologist and professor of electrophysiology, medicine, and preventive medicine at Northwestern University Feinberg School of Medicine. Rod is currently the director of the Center for Arrhythmia Research and is the PI of Northwestern's Strategically Focused Research Network grant from the AHA and the NIH-funded REACT-AFib trial. His clinical and research interests are focused on the detection and treatment of atrial fibrillation. And last but not least, Dr. Thomas Osborne is the chief medical officer at Microsoft Federal Civilian, leading efforts to provide veterans with cutting-edge healthcare solutions. Tom was the former director of the VA's National Center for Collaborative Healthcare Innovation and executive director of the VA Convergence Center. His expertise spans predictive analytics, advanced technology, and the future of healthcare. Okay, let's move on. Now we have the intros through. We have a variety of topics that we thought we'd work on today, and we'd love to get Q&A from the audience as well, but we're gonna start with a really important topic that's near and dear to many of our hearts, and that's with partnerships. We have startup leaders on our panel from Amora Health and NanoWare, and I'd like to start with Venk and James to hear some examples of a successful or unsuccessful partnership, and what elements made it that way. We'll start with Venk. Thanks, Jacqueline, and nice to see everybody. We are a early-stage company, just under 25 people, and we do healthcare-at-home digital diagnostics using a wearable, and then proprietary machine-learning diagnostics. Because we started our path in provider-facing healthcare as opposed to consumer, partnerships are completely critical, not only for us to navigate regulatory and then obvious business models down the road, but also for speed. Time is the most important commodity that a startup has, and so in the digital space, while data and everything you'd like to sort of keep all on your own, you can't be expeditious without partnerships. So from both early research standpoints, when we're validating both our wearable, our signals, our algorithms, we have to partner with top-leading research institutions like Peter at Mayo Clinic. We did some good work with them several years ago in our early days. So the academic research partnerships are paramount, really getting good physician buy-in and key opinion leader buy-in into what you're doing early. Not only helps with fundraising when you've got the backing of these key opinion leaders, but again, speeds up your validation to reach certain milestones. And then when you become in the commercial side, which we are very recently commercial, we can't sell into health systems today. We do full cardiopulmonary assessments at home, but there's no existing reimbursement for that. So we actually are partnering with contract research organizations or CROs who can use our at-home tool for their customers to evaluate safety and efficacy of new drugs that are in phase three trials or post-market registries for devices and drugs. Those partnerships were seeded only after those initial partnerships with academic research organizations. So now we have the data and the data services to sort of show the CROs and the sponsors. We're getting so much real world evidence that wasn't available to them beforehand. So partnerships are completely critical. They're gonna continue for us throughout our journey. So it sounds like academic medical centers and healthcare adjacent stakeholders like CROs is a good place to start. You have a similar experience at Memora? Yeah, thanks so much, Jacqueline. So my name is Jamie Colbert. I'm the chief medical officer of Memora Health. We are a intelligent care enablement platform. So we are a AI enabled software that allows healthcare teams to augment their care of patients when they're not within the four walls of the healthcare system. So that means that we can help support patients as they're going through their pregnancy journey by doing automated outreach, by intelligently triaging symptoms that they're having, by responding to patient questions in ways that are protecting the care team such that they work more top of license. And the use cases that we now have deployed are around two dozen right now. Everything from cardiac care to oncology care to transitions of care. So when we think about who to partner with, we are really focused on health systems as our core buyers and looking for those health systems that truly want to innovate in how they deliver care and looking at using technology to maximize the value that they're getting out of their staff. We know that there's a crisis right now in the healthcare delivery systems in terms of retaining staff, staff not feeling like they're able to work top of license, having an administrative burden that is just overwhelming and not allowing patients to access care that they need. And so that's where Memora comes in. And we have been very successful at finding the right partners who truly want to use technology, who are willing to put in the time to look at solutions like Memora to figure out how they can innovate and really move the needle on care outcomes for patients as well as creating the efficiencies that they need for their staff. So for us, it really just comes down to finding partners who really are looking to be innovative, who want to move quickly, who are willing to try out technology because they know that there is a better way and the status quo for how we do things today, how we're delivering care is not effective. And that includes both academic partners where we have research partnerships that we have done with the University of Pennsylvania, with Stanford, with a number of other key academic medical centers across the country, but then also community hospitals and for-profit hospitals who we've worked with because they have key clinical leaders who understand the value that technology such as Memora can bring. And I think the common thread is you have to eventually get to business development and the administrators at these institutions, but you start with the physicians. You have to start with physician buy-in. From your perspective, Tom, working at the VA and Microsoft, I can hear, that's great too. Do you see any new trends emerging in how companies have collaborations with healthcare providers and any challenges that you see ahead? Oh yeah, so okay, so for sure. Well, first of all, thanks for inviting me. This is a fantastic conference, so many amazing people. There's no shortage of challenges to solve in healthcare. That's for sure. That's the great opportunity and responsibility, I think, for all of us. But as far as partnerships are concerned, what we were doing and what we have done, what I did in government and what I'm doing now in industry is leveraging the power of collaboration, like you were saying. And healthcare is a team sport, clearly. And we can do and we will do so much more, so much better when we work together. And as healthcare evolves, it becomes more and more complex, and you really need people who understand domains and specialties. And if you don't do that, you're gonna miss out on opportunities. And so when you're looking at any particular domain, it's, even in healthcare, you think about healthcare, okay, you got like a provider, and it's no longer a provider, it's, you know, then it's a pediatrician or a cardiologist. And other parts of the ecosystem are the same way. You know, you don't just have a data scientist, you don't just have an epidemiologist or a statistician, you need a team of all those types of people. Otherwise, you're gonna miss out on opportunities, and you're gonna have blind spots, and, you know, then the technology is the same way. So when I was at different institutions, we would look for the things that we'd need to do. We're just, like all of us, trying to solve problems that people want addressed. And then you understand what needs to be done and then you look for partners that can help fill that gap. And then it's an exercise of searching for the right people and the alignment to make that happen. And then obviously the challenges of trying to translate between many different people that speak different languages. But I think as healthcare becomes more and more complex, as the data becomes bigger, as the domains become more interesting and the challenges are greater, then the need to have these partnerships, these complementary partnerships are gonna be more profound, more important, and we're seeing more of that. And I think when I first got into government, a lot of people said, oh my gosh, don't bother, it's not gonna work. And I was like, no, no, no, look, we're in Silicon Valley, we have the opportunity to work with some great companies, let's do this. And everyone said, no, don't bother. But we did it and we had some, it was not easy, but we had some early wins. And that just built on itself. And so now we're moving from the point where it seemed like it was a really difficult thing to do, where it's becoming more and more common, people are becoming more acceptable, and it's now becoming more of the standard. So the doors are opening up. This is such an exciting time for that reason. And also because of the great demand and the need that's really pushing people over the edge to actually do things that they wouldn't otherwise do. And because the technology is so appealing that it has the ability to solve problems in new ways that people need addressed. So, yes. It seems like it's a combination of both your Rolodex, the people you know, but as this becomes more mainstream, there are systems in place, right? And maybe, you know, Peter can talk to what systems are in place at Mayo to help facilitate kind of entry by startups. Yeah, great. A good question. So first I'll say, I think partnerships are absolutely essential to what we do at Mayo Clinic and other healthcare organizations. If you know anything about Mayo, it's a large medical center in a very small town. And we have over 200 cardiologists and 100,000 people who live there. So we need to offer something that is materially different than other healthcare organizations around the world. And in the past, we've been able to innovate ourselves and recruit good surgeons and distinguish and differentiate on those grounds. But really what it takes these days is working with partners across the broad ecosystem in innovation space. And, you know, we worked early on with NanoWare because we knew the patients were wearing halters, but they didn't like the leads. We thought maybe we could get a textile that would give us high quality signals. You guys were an early stage company at that time. There you're working with frontline clinicians and me at the time as a cardiologist and director of the ECG lab. Memorial, we realized that we had a lot of healthcare providers who were tethered to their computer and answering inbox messages and not providing the care that they should be providing direct face-to-face high quality care. So we look to you guys to help us solve that problem of workforce development and burnout and so forth. And, you know, with Microsoft as well, we've done all sorts of work. Mayo tends to have a centralized structure for these things. So we have an office of business development that manages these strategic partnerships. And what we look for are critical unmet needs in our practice or with our platform or our labs or all of these various verticals within our organization. And then we go to the market to figure out how you guys can help us solve those problems. And the sweet spot for us is if we can solve a critical unmet need in our practice, and in doing so, bring some of our own know-how to the table, co-develop, get a solution that is going to be custom suited to us, and something that we can then participate downstream as that generates revenue outside of Mayo Clinic. That's sort of the way we approach it, broadly. So it sounds like all the startup folks in the room should come to you. Come to me. At the end of this talk. Great, yeah. Okay, great. We solved that problem. We're gonna move on to another topic. Reimbursement, coding, coverage, payment. For some companies, this will be top of mind. The bio-design process would argue this should be top of mind when you're thinking about your need statement. We have expertise here from Dr. Arnold, Renee, about trends in CPT coding that relate to digital health stakeholders, and we'd love to get an update from you. Oh, you're right. Thank you. So the whole idea of the, as you said, the coding coverage and payment is people think, oh, I've got a code. I'm great. I can just go to the market and I'm gonna get all the money I need. And it doesn't work that way. You have to make sure that the insurer will cover you with that code and that the payment is commensurate with what you'd like the price to be. Since the pandemic hit, starting 2019, 2020, it's ironic how codes for telehealth in particular, all of a sudden, became available. It seemed impossible before. Well, I don't know if we could do this. And then they became available, and there are several different codes. I've got a few little slides in your app there. And some are, and they're mostly CPT codes, but not all. You're probably familiar with CPT codes. Some are HCPCS codes. If they're inpatient, it's ICD-10, ICD-11 codes. But it's the CPT codes that really talk about monitoring and patient management. And some of the ones that you may or may not be familiar with are remote physiologic management, RPM, remote therapeutic, and that's when patient data are uploaded by patients remotely. Remote therapeutic management is, sorry, remote physiologic management. These are clinicians that make those data available and by the name, physiologic, heart rate, things like that. Chronic care management, you're probably familiar with now because that is not going to be remote. Patients have to come in, and they have to have several different chronic diseases, and that's really coordination of care. And so these are all codes that you're hoping will be available to you for reimbursement. But when I try to figure out, I am a clinician, and I take care of patients. And sometimes the codes don't really link up with how I want to use this technology. And sometimes I scratch my head, I'm like, why am I being told that I need to get 16 data points to bill for a Cartier Apple Watch reading and bill for it as a monthly monitor? I think they're trying to fix that, right? Right, so those are the sorts of things where I think that we clinicians should be able to think that we clinicians feel, wait, we don't want to use the technology in that way. We want to use it once a year to keep that patient out of the emergency department, and we want to bill for that time as needed. So sometimes I feel it's the codes that are hampering the adoption of this technology into the everyday usage in our clinical practices. Right, so that's a good point. And one of the questions that was being posed to me is what are the challenges? Some of the challenges are, first of all, some of these things sound remarkably similar. RPM, RTM, which codes do you need? Which codes do you use for which time? As you said, I don't want to go, I don't want to monitor this four times a year. I don't have to. Little by little, this is changing, just like with the ICD codes. And every time that changes, they expand dramatically because now the clinicians have said, well, you're not giving me a code that meets my patient needs. So little by little, that's being done. And the FDA is also doing a lot from that standpoint for breakthrough devices in particular to improve reimbursement and having some interesting programs available. One is called the T-SET program, and it's basically coverage with evidence development. So the idea is that you don't necessarily have to have all the data available right then to now be reimbursed. So that's the other thing, is somebody mentioned very early on, I think the first set of speakers, that this is a country of innovation, and we really have a lot of innovation available to us very quickly. And I think from a government standpoint, since I work for the NIH part-time, we're really all trying to respond to those needs. I'd love to hear your perspective from the startup lens of how do I make or not make CPT codes work for me? I love your preamble to this. I'm a first-time founder, and I think for a lot of us in the startup ecosystem, you have to break up what your milestones are when you're creating something. So you create a minimum viable product, then you sort of validate it with physician community, working with Peter and group, and then you sort of prep for FDA. If I could have done it all over again, I really would have been thinking in parallel at the very beginning on reimbursement strategies, because once you get the approval, to Renee's point, you can see codes that might fit the bill of what you're doing, and then you read the fine print, it's like, oh, it has to be monitored 16 times a month for you to get that $60 or $70, and then you cobble them together. So almost by accident, we kind of fell into our contract research organization model where sponsors and CROs would pay us to evaluate their own products, and the beauty there is we're collecting that real-world evidence in certain disease states. So one day, we can go to CMS and say, we've got data on these 2,000 patients with labile uncontrolled hypertension, for example. We need a specific Novitas code here. We didn't know that in the beginning. It sounds like we are very smart with our business model. This was literally walking around a bunch of corners and being like, that reimbursement pathway is not gonna work, this reimbursement pathway is not gonna work, this isn't gonna work, and we didn't figure that out on our own. We figured it out by talking to potential users that were like, I love this, but I can't get reimbursed for it, so I can't use it. So, Mike, with your experience at McKinsey and as an operator at a lot of public companies, when you hear the frustrations of the innovators, the providers with these coding coverage and payment, do you have any ideas about or experience with different business models and recommendations for entrepreneurs as they're moving on this journey? Yeah, so thank you for having me here. It's wonderful to be a part of the conference. As your introduction mentioned, I've worn a number of hats over time. I've been in the shoes of a number of you. I was CEO of a venture stage structural heart company. I've also worked in environments where I was scaling companies and then now serving a lot of the companies that would actually be acquirers for these technologies. I think there is a theme that goes through a lot of the work that we do now with our clients, which is a view towards scale. We work with a lot of companies that are in a position of looking at these technologies to keep very close tabs on the innovation ecosystem, and they're becoming more and more mindful of falling into pilot purgatory, where they have this backlog of interesting and yet difficult to scale technologies that ultimately don't deliver on the promise that they thought they were going to have. Some of that comes back to reimbursement challenges. Some of that, though, comes back to the partnership question that we were having earlier of how does this dock into the existing workflows and the major systems that run through hospitals? We cannot have, I can't viably go to a hospital customer and say, hey, good news, I'm going to be your 38th platform that you're going to be installing and supporting at your hospital. So this view towards, okay, what's my pathway to scale, and that pathway to scale may be through reimbursement. It may be that it's more of a traditional B2B model, and you say, I'm not going to try to create a dedicated code and pathway into a payment mechanism, but I'm going to exist within more of a B2B environment where my customer comes to CRO, or it's the hospital, or it's an outpatient clinic, or it's something else like that. And that will evolve as you get smarter on your technology and as you see it come through. The quicker, though, that you get clear on what your value proposition is, I think the easier that becomes to understand, because if you have a primarily clinically-based or safety-based value proposition, that's going to be different than saying, I've got an economic play, right? I'm going to come in and my, I believe I'm going to be eliminating clinic visits, I'm going to be simplifying and streamlining, I'm taking out work from the system. Once you do that, it's going to inform, once you've got clarity on that, it's going to inform the data that you gather, it's going to inform how you go out and try to position your product, and then ultimately where you think it goes. I'd like to shift gears, because we have a lot of interesting topics of note. We're going to go to evidence generation, real-world evidence generation, and kind of the speed and pace at which VC or your academic partner might expect to generate content and outcomes. This is one that is for Rod. I mean, you've worked with lots of partners on clinical effectiveness, could be consumer or enterprise grade. What are some of the challenges of conducting the research in the field? Well, I'll tell you some of the easy parts. Some of the easy parts is, you know, you could buy a, you go online and buy a couple of watches or a couple of cardio devices and get your research program up and running for a very, very small amount of money. And since the stuff is already in the public domain, you get it through your IRB pretty quickly. I think that the challenges are that the system is calcified in many ways. So, you know, I'll give you an example. We, every time we saw a patient in EP clinic, we would get a 12-lead ECG, and we timed it. You know, it would take about 13 minutes for the tech to come into the room, disrobe the patient, hook up the ECG, and do that recording, right? And the number one complaint from our patients is, my doctor doesn't spend enough time listening to me, right? Well, how could you spend a lot of time when you have a 20-minute visit or a 30-minute visit, and 13 minutes has been consumed with a piece of evidence that you may not need? So the question is, you know, could we do a six-lead cardio, right? And can we save time? And do we lose information in doing that? So I think that then the answer was yes. You know, we could shave off, you know, 25% of the time, even when people were not used to that technology. And you know what? For the most part, in return visits to EP clinic, you don't need a 12-lead. We're not that interested in ST segment changes, it turns out. We're interested in rhythm and intervals. So, but how do you, but could we implement that? Probably not. Why? Because the system is just ingrained in the old way of doing things. So unless there, to me, a economic piece of that, right? That by saving time, we could see more patients in clinic, or we could spend more time with our patients, and prove that our patients are happier doing it. It's very hard to move the needle in things that are sort of baked into our everyday activities. Are your patients doing the cardio before, in lieu of a 12-lead ECG? So we have finished that study, but we, and now we're sort of selling it to clinic, right? That this is something that we should be doing routinely. And I think that, you know, every room, just as they, in the old days, had an ophthalmoscope, and still have a blood pressure cuff, well, maybe every room should have a six-lead cardio, because if you go to primary care, there are years that go by without patients getting an ECG, right? So a patient comes for a pre-op exam for knee surgery, is found to be in a-fib, and the last ECG was done four years ago. So, you know, can we use this easy, inexpensive technology to gather important information on patients, and do it, and save money, and improve care? So I think that that's incumbent on us to, as academicians and clinicians, to take that technology, and to ask those questions. Tom, you're well familiar with all the different stakeholders in the room, and getting everyone to, you know, sign on the dotted line. Do you have any thoughts, from your perspective, at the VA, or Microsoft, on how to generate real-world evidence these days? Oh, thanks, Jackie. That's an awesome question, and I do have a lot of thoughts. Please do, we got 15 minutes, and 32, 31. I'll try to make it concise. So, no, first of all, I mean, I think it's really important that we not only do research, that is sort of clinical, you know, where you have a very structured environment, where you know exactly what you're doing, and the patients are controlled. Not only that, but real-world evidence, where you actually see how it's gonna work with an average person, who has an average life, who doesn't necessarily follow the rules, and is in a structured environment. So real-world evidence is super important, and it's actually, I think, appealing, actually, if some of you are in startups, to do that, because it allows you to get a different perspective on how your tool can be used, and also can get you closer to actually deploying it in a real-world environment, which is really your end goal. The challenges, though, I think, if you wanna really move fast, which I think everybody who's got, you know, limited time, and energy, and resources, yeah, exactly, you kinda just wanna go for it, and I think that's a real danger in this space. It's a danger for probably five reasons, but I'll talk about a couple of them. One is we can do harm with good intent, and that's the last thing anyone wants to do. You certainly don't wanna hurt anybody, and that'll tank your company anyways. And so getting a lot of the right key stakeholders together, whether it's nurses, or doctors, people on the ground, they'll tell you about the pitfalls, and the landmines, and they'll give you insights that you just would never have expected. So that's super key. The other thing is, start with the end in mind. Understand what you're trying to solve, because, like, what is the value? What kind of value are you trying to get out of this? Is it cost, is it satisfaction, is it outcomes? And if you don't know, that's trouble, but you gotta know what that is, because if you don't know what you're measuring, and if you're not gonna measure it, and if you can't measure it, you can't actually see or tell if something actually has value. And so you actually have to know what you're looking for before you do it, because if you don't collect that data, then you'll never know if you actually done the right thing, and you'll never get it published, which is what everyone cares about. If you wanna have that currency of credibility. So I could go on, but I think those are the big ones that are top of my mind. Jacqueline, if I just jump in quickly. On real-world evidence, it's gonna tie a couple themes here. Obviously, shameless plug for nanoware, but home-based is kind of the definition of real-world evidence. There's only limited information that someone like Rod is getting in a 13-minute visit with the ECG on-premise. And another probably more prominent example, we recently got an approval for the first AI diagnostic for hypertension and continuous blood pressure at home, self-administered. All the physicians on here, I mean, could argue whether it's a real thing or not, but white coat syndrome, when you go into a physician's office, if you're nervous, your blood pressure could be high, is that real world evidence. And the second thing I'll bring up is that this is a very big thing for FDA now and down the road CMS, as Renee mentioned, it's not just real world evidence that you can collect from some EMR where a patient population might be pretty granular or vanilla. You're going to need evidence on a minimum of 15% black Americans. You're going to need evidence on a minimum 19% Hispanic Americans. You're going to need 50-50 on male and female. You're going to need ages 18 to 80, not just for the validation of it to get through FDA, but CMS is going to start requiring that as well with more at home tools. So I think this rapidly evolving evidence generation and touching upon some of the points that Tom said, I think is going to be paramount to any of the stakeholders as we're thinking about partnerships, but particularly for the startups coming forward. I love dynamic conversations, so let's just keep going. Go ahead. Renee? No, just in terms of unmet need. So you should be looking at RWE from wherever. It could be published data, it could be your EMR data, it could be your claims data, it could be from Medicare claims data, but you don't want to be a device or a drug or whatever looking for a disease. You want to demonstrate that patients are coming back more frequently, the outcomes are not good. You might be the 20th of 20 asthma medications, but if you can demonstrate that patients aren't coming back as frequently, they aren't hospitalized, they're not dying as quickly, or they're living a good life, it's a good quality of life. Quality of life is a very good indicator and can be an indication for FDA approval, which people don't realize. And so you've got to start with, I think, with real-world evidence, and then continue all the way through. What's your favorite quality of life measure? Well, some people talk about the EQ5D, because you can develop a cost-effectiveness analysis with those data. So there's non-disease specific and then there's disease specific, and it's good if you're doing a clinical trial to have at least one of each. Yeah, I was just going to jump in to say that I think there is a place for academic research to help prove out the value of digital health and new technologies. But at the end of the day, what really matters is adoption, right? And so whether or not we have 20 real-world studies with robust evidence or not, if clinicians don't feel that this technology is actually making their lives better, then it doesn't matter, right? You've just kind of padded your resume as an academic and you've gotten some papers published. But really what matters is do we have the data to show that the clinical teams feel that this is making their day-to-day better, right? So if you were to tell them that we're now taking away some technology, are they going to be up in arms, right, and put up a fit and say, no, no, you can't take that away from me because, you know, A, it's going to make things harder for me, I'm going to have to do more work, or B, you know, my patients are going to suffer if you take that away, right? At the end of the day, that's what matters, right? And on the patient side is the same thing, right? If you give a patient some new technology or some tool, some digital health intervention, and at the end of it, they say, you know, that was interesting or I liked it, but then you tell them it's not going to continue and they say that's fine, well then that also may not matter, right? But if the patient says, wow, this actually really helped me to feel more supported as I navigated through my CHF journey, well then at the end of the day, you have something that really has the stickiness that's needed to truly change behavior, right? And so kind of where we are with Memora, right, I mean that those are the things that we're actually looking at with each of our partnerships is to see, you know, what is the true impact on the day-to-day for the clinicians who are interacting and what is the day-to-day look like for those patients? At the same time, we're always setting up success metrics from the beginning. We're always making sure that we're aligned between our team and the health system team as to, you know, what are the pain points? What are the problems that we're trying to solve with this technology? And we're tracking that using a dashboard that is very transparent and allows all parties to have insight along the way such that, let's say we have a 12-month contract, by the end of those 12 months, we can all be very clear as to whether or not we accomplished the objectives that we set out to accomplish. And Jamie, a quick plug for you and Memora so you can send the commission check whenever you're ready, but I think you're bringing up a good point that we haven't touched upon on real-world evidence. Compliance and your adherence to your care coordination platform is paramount in that regard. Dr. Noseworthy could see a cardiology patient and if that patient isn't following his care coordination plan, the real-world evidence is kind of meaningless at that point and can't be standardized across his 100 or 1,000 patients if you're looking for trends on a particular disease state like heart failure. So for us, it's a little bit different because we're in a CRO environment that's more controlled. These patients are opting in and they're following a protocol of 48 visits over four years. But if and when we move into the health system environment, we would almost have to use or partner with a company like Memora to ensure that compliance and care coordination from that RX or that prescription, even though we're a healthcare-at-home platform and we're the greatest thing from sliced bread, if it's not being used as it should in care coordination, we're not going to generate the outcomes that are needed there. So again, I think a nice plug for the multiple stakeholders and sort of it taking a village. I'd love to get some thoughts from Mike and Peter as we end the conversation. Yeah, I think that every patient interaction should contribute to real-world evidence and we should try to blur the distinction between clinical care and research. So a traditional model is there's research that happens in a silo under an IRB with its own infrastructure. It's costly and inefficient and you can answer one or two questions at a time. And then there's clinical practice, which is everything else. Mayo is trying to solve that by the creation of Mayo Clinic Platform, which is a completely de-identified instance of all Mayo Clinic data and it's live and continually updated. It includes multi-omics, imaging, ECG data, telemetry, digital pathology, the whole thing. We have problems with diversity of the population served by Mayo Clinic. So we have a data network that includes Brazil, Canada, South Korea, Israel, a number of other countries. And the hope there is that we could build towards a future where almost anything in clinical practice where there's equipoise, you could have a point of care randomization. And the way the tech world is constantly doing A-B testing, we should be doing that in medicine and we should be learning from every patient that we interact with. So create sort of a learning health system where every interaction and every data point contributes to making medicine better. Any thoughts from, you know, advising companies, big and small, around how to approach not only the inputs, but the outputs to real-world evidence generation? Yeah, I'll just highlight a couple of challenges that we tend to see our larger med device clients struggling with. And this is one where, candidly, they're not very good at it and so they need all of you to help them. The first is, you know, you're now seeing new technologies being saddled with enormous post-market requirements. Like, you know, new technologies that are coming out where typically you would have a rather small controlled follow-up survey or single arm. So you're now sort of seeing huge numbers and huge requirements for data that in some cases are catching companies quite off guard. And so the ability to have technologies that allow for that to be gathered for the data to come in in a way that is simple and structured and can actually lead to them taking down the burden for some of those, I think that would be well received. There's the second challenge around label expansion and the work that you do to be able to say, look, we're taking real-world evidence. I've heard some folks refer to it as patients in the wild, that, you know, you're actually seeing the way that it's going and you're able to leverage that into expanded labels. And then the final one is the feedback loop into their R&D organizations, which is, companies love to talk about that. They're very poor at doing it. They're very poor at actually closing the circle to be able to then influence back into what's the next generation going to look like or how do I inform my pipeline? So to the extent that there are solutions that are offered by the work that you all are doing to be able to solve one, two, three, multiple of those, I think it'd be very well received. I'm quite excited to hear about all the different ways that we can kind of naturally or effortlessly collect data, right? Whether it's consented clinical care where we're gathering data or if it's retroactive through registries or if it's in the moment consent, it's at home. I think there's lots of opportunities, particularly with AI to kind of make sense of all the data. And I think we're looking forward to the next conference where we can come back and talk about how we're collecting evidence. We've touched upon partnerships, reimbursement, adoption, real-world evidence generation. We have a couple minutes to take questions from the audience. So I think we will do so if anyone has questions. Yes, no? Okay. I think... All right. This is a quick question for established, what I want to say, big players like Microsoft and Google. So when you're looking at a bigger piece, not a small piece like startups, how do you what do you want to see with the startups for you to expand partnership with them? Good question. So Microsoft is really an enabling company, you know, providing the tools, the technologies, the support to help others be successful. And Microsoft is not as much into a space of actually doing that work and actually not being a healthcare provider but helping others be successful in doing those things. So it's very much up the alley. Microsoft's a big place and there's a lot of different tools. There's a group called Microsoft for Startups that you might be interested in. And there's a couple of programs associated with that. And Microsoft also has a venture capital arm, M12. And quite frankly, there's many different ways to collaborate with Microsoft. And like many things, it's about, you know, making the connections, making finding the alignment, finding ways that there's a mutually beneficial relationship. Not sure if that answers your question, but happy to talk more about it if you like. Yeah, I think it's very comparable in terms of the way the companies are structured, right? There's philanthropy, there's cloud customers, there's an explicit startup community for philanthropy. I think working in the hardware division, there's always a search for deep talent and expertise. So sometimes it's not the IP, but it's the talent that they're looking for to bring in to their engineering teams. Because witnessing very large acquisitions and working on them myself, it can be very difficult to co-opt into a large company and sometimes it's much easier to bring on the staff, the brains, rather than the IP. So that's kind of my experience working with Pixel. Okay. We'll keep going. Yeah, just for... Hey, I was just wondering, you might have covered this already, I'm coming in a little bit late, but how do you think the implementation might go for differently abled people? So any experience working different types of users with your... I mean, I can take that first. So we are an SMS-based platform, and we primarily interact with patients through their phones using SMS. And that's by design. We don't have an app, we don't have hardware. And the whole idea is that we want to reach the broadest population possible. We are seeing engagement rates of 95% or higher using our platform because we know SMS is just so intuitive to all populations. We get asked, well, what about older patients? Well, we see that the 65 and older population is actually very facile with text messaging. What about patients who speak other languages? Well, we can translate into other languages and they can still interact using text messaging. We can also enable caregivers, such that if you have a patient who, because of a disability, is not able to communicate in a way that's easily translated into a phone through SMS, then they have a caregiver who can help to support them. So that's kind of how we would address that question. Would love to hear answers from others on the panel. It's a great question. We still struggle with certain populations. We have a self-administered wearable. It's like a beauty pageant sash. It goes over your right shoulder for an hour, an hour and a half. In all of our usability studies, not only for FDA, but for real world evidence generation in eventual reimbursement, you do need to show that this is not just cherry-picked populations that have enough dexterity that they can, it's, you know, for us, it would be simple to put on. But there are people that have very limited mobility that we have to build something new in our app, like a lot more instructional videos for a caretaker or a child, you know, like a son or a daughter or someone to help on that self-administration. So a lot of these things that you may not be thinking about in your first minimum viable product build, we had to address sort of retrospectively. The good news is, as Jamie pointed out, I don't like hearing this idea of like, well, people are not technology savvy. I think the pandemic really, really pushed people of all ages and locations to become technology savvy. And even in the lowest socioeconomic environments of our country or the world, everyone's got a smartphone still now. So I think instructional and using digital tools, such as, you know, with the ability with AI-generated videos, when you can kind of give inputs, we can augment that coordination for people of different ability physically to address it. But that's going to be a constant thing that we have to address and stay focused on. Thank you for your question. Thank all of you for coming, your time, your expertise, really valued as we discussed the innovation last mile. And thanks for your attendance.

Digital Health

Innovation

Partnerships

Reimbursement

Real-World Evidence

Adoption

Accessibility

Technology Integration