Episode 1: Inaugural Episode

In their pilot episode, Gabriel and Mark provide a basis for future shows and discuss a diverse range of healthcare topics and opportunities for innovation. Get to know your hosts, how they’re wired, and their enthusiasm for the most important work in the world — to 10x the quality of our healthcare.


Mark Redlus [00:00:15] Welcome to the 10xTotalHealth podcast hosted by Gabriel Eichler and Mark Redlus. We’re so excited to bring you our unique perspective on all things that we come across changing and more importantly radically improving the healthcare landscape. The show notes will be made available to anyone interested on our website at 10xtotalpodcast.com. That’s one-zero-ex-total-podcast-dot-com. To learn more about me, and/or Gabriel, please visit our website. If you have an idea for a great guest, a transformative company or a leader, please drop us a line. We look forward to hearing from you. Now, as my favorite podcaster says, without further ado please enjoy this very fun and interesting inaugural episode of the 10xTotalHealth podcast.

Mark [00:01:10] All right. Welcome to the podcast. This is going to be quite an adventure. I’m Mark Redlus [Gabriel Eichler] and I’m Gabriel Eichler.

Mark [00:01:18] All right. Well we’re going to introduce one another because we think that that’s probably the best way to do this. Be more interesting that way. Sure there’ll be at color and we’ll then talk a little bit about what we’re gonna do on this introductory podcast that’s gonna serve as our baseline before guests start arriving. [Gabriel] Episode 1.

Mark [00:01:41] Whoa. OK. It’s a lot of pressure. Episode 1 is always a strong prequel. OK. So, it’s hard to do it after that, but this is funnier this way. Dr. Gabriel Eichler — let’s talk about Gabriel for a second. Doctor Gabriel Eichler is a work-stream lead at the Kraft Precision Medicine Accelerator at Harvard Business School. He’s also the founder and managing director of Oak Health Partners, a boutique consulting firm focused on data and technology strategy. More than 20 years ago Gabriel — who is originally from the Boston area — came down to Philadelphia, where he earned his bachelors in Computer Science from the University of Pennsylvania. He completed his PhD in bio-informatics from his research at Harvard Medical School, Boston University, and the National Cancer Institute. He has been consulting to and serving life science and healthcare organizations for nearly 15 years including working at McKinsey, PatientsLikeMe, GNS Healthcare, and several others. I would consider him one of the certified smart guys that I know. I know he loves when I say that. He’s also an experienced speaker, having delivered over 100 lectures globally and authored over 40 peer-reviewed manuscripts. [laughter] I’m just thinking back to — this is perfect for the podcast — thinking about when we originally did your intro… a thousand lectures, globally. [laughter] Only a hundred for Gabriel in this particular bio. [Gabriel] It’s called 10x right there.

Mark [00:03:06] 10x — he 10x-s his lectures and authored over 40 peer-reviewed manuscripts and two book chapters on a wide array of healthcare technology domains. [laughter] Okay I’ll make it. He also co-hosts another podcast at Harvard Business School which will not be as good as this one, I’m sure [Gabriel] called Under the Data Scope.

Mark [00:03:25] Oh there you go. Perfect. But honestly, really excited to get to do this show with you Gabriel. So, this is a lot of fun.

Gabriel [00:03:34] Mark, it’s a real tremendous honor and pleasure to be here with you to record this. Let me introduce you if I can. So, Mark Redlus is the chief executive officer at Tridiuum, a digital behavioral health technology company based in Philadelphia where we are recording this today. Products aimed at 10 Xing patient and provider experiences that overall improve the health and wellness of patients. You can find out more about Tridiuum at Tridiuum-dot-com. That’s Tridiuum — t-r-i-d-i-u-u-m-dot-com. And I’ll add my personal twist here which is that Mark I find to be a visionary leader who reimagines how healthcare and technology can work together. And it’s always an inspiration to sit and hear from you Mark about the kind of things you think about. I don’t know where you get your inspiration, but I have to say it’s quite impressive.

Mark Redlus [00:04:27] Wow, I really feel like I under shot on yours then. Thank you. [laughter] In case you guys out there aren’t getting this we obviously enjoy spending time with one another, and this will be as much fun probably more fun for us than it will be for you. You’ll be thinking “what are these guys talking about, is it even making any sense.” So. But we’ll probably tag team here a little bit talking about what this podcast is going to be about.

So, it’s really organized around 10-xing what healthcare experience and innovation is for patients, but also for providers and really all the aspects of a system — administrators, other stakeholders that are involved. We’re interested in breakthroughs in that area. And it doesn’t have to be just technology, it can be process innovation as it pertains to healthcare. But I don’t know, Gabriel — if you want to add anything to that?

Gabriel [00:05:26] I think that right now there exists a gap between the technology innovators of the classic tech sector and the practicalities and realities of implementing healthcare technology, because it’s very hard to put technology to work on massive complex populations and in really complicated care settings. And I think that that gap is one that we want to explore and codify and understand and ultimately help other innovators improve how they address through the technologies that they’re trying to bring to market.

Mark [00:06:00] Yeah. Really well said I. I think, you know, both Gabriel and I have an incredibly unique privilege, I think, to get to work with, see, work alongside of, and serve various stakeholders in this kind of healthcare ecosystem, and probably have — we definitely have — different takes on those experiences and what’s working, what isn’t working, and really getting to talk to a lot of folks that are working hard on, well, on hard problems. I think that’s — I mean healthcare is a hard problem in and of itself. But I think it’s really interesting to see how people are thinking through what is really kind of — I love this phrase — like an eight-dimensional chess game is what it feels like sometimes working out there. People are like, oh you’re not rocket scientists…Have you ever been in healthcare? Because it feels like you’re a rocket scientist when you’re functioning in this environment. It’s complicated.

So, we’re going to talk a lot about that, and the guests that we have on you know, I think we’re gonna try to have a unique guest on every two weeks. We’ll try to release these every two weeks from various health systems, technology companies, experts innovators, that’re working on these problems and most importantly that are having an outsized impact on the population in patient health out there and that experience. So today’s just kind of just an introduction, just a “get to know us” podcast of a couple of jumping off discussion points that we’ll kind of riff on, that’ll give you an idea of thematically about what we’re thinking about and the work we’re doing.

It’s always good to, while we’re not trying to discourage anybody from listening to this — if you’re a musician and you want to hear this that’s great — but you know we’ve kind of put together an ideal audience for the podcast. If you’re a healthcare executive, a digital health innovator, a product manager for software or technology, consultant, subject matter expert, policy maker, investor, or just a budding entrepreneur who is trying to break into this, I think there’ll be something for everybody — at least from episode to episode in this — and hopefully you’ll get to sit in on some of the very cool conversations that Gabriel and I have every week. You should get the general sense of how much, really, we enjoy talking with one another and working on super challenging problems. We do talk outside of this podcast, probably a couple times a week. I know, it’s surprising.

But we know we’re thinking about things, Gabriel’s thinking about things in artificial intelligence and data science that can really move the needle on outcomes. He’s working across a variety of disease categories and, while he works with me and behavioral health stuff, he works across a lot of stuff that I’m sure is going to come up as we go through this.

Gabriel [00:08:46] And I think, Mark, your experience about launching products into mature care settings and trying to innovate and replace broken care systems with technology-enabled systems is really a set of experiences I want to learn here from you about, because you’ve been doing that more than anybody else I know, candidly, in terms of rolling this technology out so nationally and so impressively at large scales. So that’s going to be great.

Mark [00:09:12] Yeah well, thanks, it’s going to it is going to be great. We’re gonna have a good time. So, with that we’ve introduced ourselves, talked a little about what we’re gonna do here, maybe we’ll hit a couple of jumping-off points. Somehow, we’re gonna fit in his Peloton into this conversation because I’m always curious about… [Gabriel] Well it brings in wellness — right next to health is wellness.

Mark [00:09:33] So proactive — you’re right ahead on everything. Want to see the coolest technology? It’s staying ahead with Peloton. Maybe we’ll get them as a sponsor [laughter]. [Gabriel] So, let’s just go and say this this podcast brought to you by Peloton.

Mark [00:09:50] We just did it. [laughter] Not monetizable. OK. We’re gonna keep going. So, I think to start, we had a lot of cool topics that we were doing in our test podcast that were really interesting and fun to do. A couple these we can we can pick on. You know, I just mentioned AI, and we just came from a lovely lunch meeting with an area academic healthcare institution talking about research and things like that. A lot of curiosity around AI in healthcare, talking about programming for next year and some plenary sessions and things like that. So, you know, you get a front row seat with what you’re doing working at Harvard looking at AI. And how are you — I shouldn’t say how are you — what. What are you seeing that’s really blowing your mind right now in AI in healthcare, if anything?

Gabriel [00:10:49] Well, I’ll tell you something that I’ve been observing which is interesting. So, you know, we often have this sort of classic hype cycle curve where things go from and initial technology to a mature technology over the course of, you know, between six months and six years, right. But typically, it’s in the years — multi years.

And what I found in watching this whole segment and sector is that where AI has been able to actually prove its value and integrate into existing workflows, it just takes off and no one thinks twice about it. So what I think about, for example, is pathology — or more like radiology imaging and image analysis — and that, you know, there’s been a few awkward years in there about “how does this thing actually work” and “where does it fit into workflows” and “what do docs think about it” and “what about liability” — all these challenges.

But I feel like the head start on that space is so far out there now for the companies that have been investing in this for years that they are really, really far along on this process of building industrial-strength, commercializable AI for the replacement of conventional pathology workflows, that it’s, you know, just taken the world by storm. And, you know, it was one of these area academics that shared with me a slide of Wiley Coyote already over the edge of a cliff but hadn’t fallen yet. You know, one of those classic cartoon scenes. And that’s the way some people are viewing radiology now is that this AI is, you know, out of the…we’re in uncharted territory, and we’re never going to go back to a non AI-enabled world in pathology or radiology, and it’s going to be interesting to watch how it matures and really takes flight over these next few years.

And I know inside the big medical imaging companies, they’re wondering too — where do they put this. Does it sit in the software stack. Does it sit in the PACS image analysis. Does it sit on the machine itself, the hardware. And these are massive questions which I think they’re going to be defining in the next coming months.

Mark Redlus [00:13:00] Hmm. I was going to ask you, like the timeline as far as uptake goes and mainstreaming that. I mean, you’re seeing kind of the front end of the curve a bit, but you feel like the front end is moving pretty fast?

Gabriel Eichler [00:13:13] Faster than I’ve ever seen it move. I mean, it’s kind of consistent agreement that this stuff is as good as humans. It’s cheaper than humans. It’s going to potentially just change workflows dramatically, right? And you know some may think that this may be transparent to a patient, but the fact now that you can have a queue of images that need to be read by clinician, that get prioritized by an AI approach, that says “well, these are the likely positives we need the human to sign off on first. These ones are less likely to be positive and therefore,” you know, “it’s the less acute situation for those patients so we can delay the reading of those images.” Right. So, it’s a fascinating…or even just the workflows, right. You know, typically patients get sort of multiple images that have to get taken for a patient in part because the image — the first one — comes out wrong. And so, if you actually have a AI feedback built into the system so that you interrupt the image-taking process to say the data coming off this device is not of high enough quality to be acceptable — rotate the patient, or change the approach, or change the parameters of the image acquisition process — this can vastly improve the throughput of what a machine can do. Because now you can get 25 patients on a machine in a day versus 15. Right, and so image acquisition is much faster, much faster usage of, you know, flow of patients through that clinic, right. Less waiting times. Cheaper, you know, use of these machines because they get utilized over more patients and more tests. So, you know, it can change profoundly all sorts of implications on how even just imaging is done.

Mark [00:14:51] You know, as a jump off, you know, and kind of rolling it back to my world that I know — certainly far better than the diverse set of areas that you spend a lot of time in or observing — it seems like there’s a couple of different really interesting subcategories within AI, in each kind of discrete either service area or what have you in healthcare, but a common theme is, you know, is there an insight component to AI where it’s just better at discovering things that are going on with patients or even at population levels. Is there a, you know, there’s obviously a workflow aspect to AI that is really blowing efficiency into the system which has its own really, as you said, positive repercussions that are going to reverberate. What do you think the lowest hanging fruit A.I. applications are really good at? Right now, as opposed to, you know, the hope of AI.

Gabriel [00:15:53] Yeah, it’s funny. You know, I was sitting down with a major pharmaceutical company’s AI leadership team. And, you know, these guys are top-five, sort of most-respected in the space. Now I was asking “where are you guys applying AI.” And they said manufacturing. [Mark] Mm hmm.
Gabriel [00:16:10] Right? So sometimes the low-hanging fruit and the — clearly — most ROI-positive investments are the stuff that’s already being done, that you think is pretty efficient. But, ultimately, the optimization of manufacturing flows is a tremendous opportunity in the pharmaceutical industry. [Mark] Yeah.

Gabriel [00:16:24] But, you know, where I think it’s really interesting Mark, candidly, is — and this isn’t necessarily low hanging fruit, but really interesting — is there are places where having a human intelligence in a system isn’t scalable. Yet there’s places where AI could be inserted because it’s practically marginal cost zero. To improve a workflow in the middle of that workflow. So, let’s just take your innovation on patient intake, at a behavioral health clinic or in a patient population that’s trying to be assessed for behavioral health needs. The ability to — using computational and digital technologies, potentially even AI — assess that patient, at that instant, without a human, a trained human, needing to be a part of the process, is profound. Because now you can have untrained, front-of-office-type staff members evaluating a patient using a tool like this.

Back of the eye imaging, same thing, right. Patients at higher risk of diabetic retinopathy of the eye, where you get nerve damage and go blind from diabetes. These images can be taken off of a machine and analyzed with AI to determine how much that disease pathology may be about…how the disease pathology may be established in that patient’s eye, and the risk of that patient getting blind or having complications from this all done by AI. That’s really, really powerful to me. You know, because it wouldn’t make sense that at every PCP’s office you have an ophthalmologist. Clearly nobody would ever think about that, but now we can screen every diabetes patient coming through a clinic because we have an AI-enabled sort of “mini doc,” if you will.

Mark [00:18:17] Yeah. It feels like, I mean, the stuff we’re working on with you — while it’s more advanced than screening — it’s…screening feels like a really powerful place where AI can just get things filtered down to a spot where you can make the best use of clinician’s time. [agreement] It just feels like the easiest thing in the world to do to get ahead of that, if you can get pattern recognition down, if you can have an active learning system, deep learning system, where it’s getting smarter and smarter, like we’re trying to do in behavioral health, I think that’s…that seems like a great utility for that.

Gabriel [00:18:55] But, you know, it’s funny because good AI requires good input data — both to train and then to be applied to. And I kind of think of AI as like a Formula One engine, right. You need extremely refined fuels that are just the right chemical mixture and a consistent mixture in order to make that engine run optimally. And if you start throwing some other fuel into that engine, you’re gonna get all sorts of odd effects and odd performance. You may not even be able to predict what those are. So, let’s just take, you know, a patient who has to sprint to get to their physician’s office for their doctor’s visit. They get brought into the exam room because they’re five minutes late but fortunately, they get seen. They get sat down the table and the first thing to do is get their blood pressure taken.

If we don’t have a human involved to say, “well, I wouldn’t really trust this blood pressure reading because this patient was five minutes late and had to sprint here to get…[Mark] Its context.

Gabriel [00:19:52] Right. Yeah, and if that context is missing…the real world is messy. [agreement] In AI we don’t really have enough data yet to know how to deal with the messiness of the real world. [agreement] So, another low hanging fruit dimension which I think is key is where are the places where we can make a really clean environment in which AI can be applied.

Mark [00:20:09] Yeah that makes a lot of sense. So, you know, rolling off of AI for a second and talking, you know I think AI’s obviously breakthrough innovation area in healthcare and across many sectors — not just healthcare, but certainly interesting application there. The other thing we talked about — again, in our test podcast — which I thought was really fun and interesting to talk about was this move of big tech coming into healthcare, the role of big tech, integrated delivery networks, risk, patient wellness, getting ahead of the game in some of these cases. You know, we chatted about…I think your question was “what would it be like…” I’m sure you can remember this question…but it was something to the effect of “what would it be like if we could have a health system that didn’t have any…knew no boundaries, like could leverage technology in the optimal way. You know, it could, it didn’t have anything impairing it, just wasn’t shackled to today’s constraints.

Gabriel [00:21:20] Well that’s interesting, right. If you think about it, value-based care — people always look at as a way of saving on cost and providing…you know, people talk about it being a riper, more fertile space for innovation, right. But just imagine how much we could close that gap that currently exists between consumer tech and health tech, if we have, now, hundreds of potential experimental environments where clinicians that are…or thousands of these experimental environments where clinicians are wanting to innovate on how to bring tech into their care delivery experience and process. How big tech can work with them or build networks of them to experiment for these things, and how much better we could get at vastly and rapidly changing the technology footprint of tech and healthcare, right? And that’s really profoundly exciting. But I think that this is an aspect of value-based care which we don’t necessarily put a value on. But…and I don’t think that, you know…

Mark [00:22:24] You mean being able to innovate rapidly.

Gabriel [00:22:27] Rapidly. Yeah, and be able to be much more agile with our innovation, because it’s not so much about who’s getting paid — it’s fundamentally a physician believes that they can improve a patient experience or outcome by the use of some technology in that process. And that physician is enabled and empowered to go do that because they’re not waiting for an insurance company to pay for it. They’re able to fund…[Mark] Or an IRB or just a lot of research structure to it.

Gabriel [00:22:54] Right. You can fall off the horse both ways. I mean we want to have reasonable patient protections of course. Right. I would hate to know my PCP’s “well, you can drive to the hospital not take an ambulance. We’re doing a study about how much we can save if we do that. [agreement] That would not be satisfying. [agreement] But the same hand, what’s the right balance there to enable the distributed rapid innovation that that distributed environment can provide but also the reasonable safeguards.

Mark [00:23:19] You know — you just stimulated about five different questions in my my brain there — and I’ll just pick off the one that surfaced to the top first. But, you know, with research — and this is something we’ve struggled with at Tridiuum — which is…which is this idea, that about a year or two ago…and this is in no way meant to denigrate the NIHs and the grant mechanisms out there, but we kind of just got the mindset that, you know, grant funding was just too slow and it couldn’t keep up with our product innovations and our ability to impact patient well-being. You know, actually impact outcomes. What’s your thinking — I mean, you just talked about being agile.

What’s your feeling about research, formal research, processes and procedures. What’s tech’s role in that. You know, thinking of Apple Kit, Apple Research Kit and other things like that but how do those two worlds kind of function in this really accelerating…you know, tech digital health kind of landscape right now, where people can make incredible improvements to products. We do that, we make incredible improvements to our products in six months, nine months. Way, way under the time that a phase two RCT be done — grant-funded RCT — that’s Randomized Control Trial for those of you playing at home. But, you know, it just doesn’t work for what we’re trying to accomplish as a commercial entity but also if you’re trying to do good. Where does research play in that? That was a really long question lead-up. But, do you have any reaction to that?

Gabriel [00:25:03] It’s a good question. You know, I mean, I think that…You know I think that this plays into the translational gap that a lot of digital companies, digital health companies, fall into, right, of this gap of “OK I’ve solved this technology problem. Now I need to prove it works and make it work at scale so that healthcare can adopt my brilliance and my innovation.” And that this gap of getting hard data on the benefits and value of these innovations is fundamentally important to the…You know, this is the last mile in the manufacturing line of innovation that we hope to be building in digital health. Question. Yeah. And how are we gonna do that. I mean how are we gonna build the translational engines that we need in healthcare to enable this type of innovation to happen smoothly and efficiently, right. [agreement] But Mark, you know I have an observation for him — I’m curious your thoughts on this. So, people often talk about this gap between consumer healthcare…consumer tech and healthcare tech, and right now just typically cruddy and poorly designed most tech is in healthcare. And I’m kind of reminded of the fact that healthcare, as a system, tries to serve the entirety of the population it serves. And that, you know, you have high socioeconomic folks, low socioeconomic folks, you have people who are quite well and healthy, other people who are at death’s door. You have able-bodied people, you have disabled people — and all sorts of disabilities, not just physical impairment but also emotional and mental impairments, you know, cognitive impairment.

So, the challenge that I see is that we already know cell phone adoption is, you know, 90 percent, but it’s not 100 percent. We already know that probably, I’d guess, 70 percent of Americans use Amazon, right. But it’s not 100 percent. Whereas healthcare has a standard it sets itself to which is to serve 100 percent of patients. And we kind of need that. It’s a universal system that needs to be universally accessible, and if you were to build a hospital and say, “yeah we just cater to people who can walk into our clinics — we don’t even have ramps at the front door.” “We have a hospital only people who can pay cash.” Even those do exist, but they’re rare. So how much do you think that innovation in healthcare is sort of slowed down by the fact that we need something that is so robust to such a diverse population that it serves, versus the consumer tech side. Amazon’s plenty happy to serve those 70 percent that can use their quality product and ignore the other 30 percent simply because they can get away with that.

Mark [00:28:02] Yeah. Wow, that’s an awesome question. This is why Gabriel is on the… co-hosting this podcast. Because he asks awesome questions.

I don’t know if I have a real great answer, other than to say that our VP of clinical science at Tridiuum, Dr. Tina Harralson, is very, very interested in trying to find ways to get our product into what would be the socioeconomically, kind of, disenfranchised. And we’ve had a lot of conversations about homelessness and in folks that are, you know, seemingly without any kind of…you know, wherewithal to basically live and…live and work. And Tina is famous for saying — I’m attributing this to Tina, hopefully she’ll laugh when she hears this on the podcast — but, she’s like, but everybody has a cell phone. Everybody. People that are homeless have cell phones and, that’s still not everybody obviously, that’s, you know, you have age demographics in there, you’ve got lots of other types of challenges.

But the phone is a really important part, I think, of the future of healthcare. And I think a lot of the folks…a lot of the folks that are working on it from a consumer tech standpoint — that are migrating into healthcare — we talked about [this] in our test podcast, you know, Apple’s doing this. You know, Apple’s not exactly the harbinger or the purveyor of really affordable technology for everybody, but, you know, they have a significant interest, which will kind of raise all ships, I think, in the healthcare tech space. But there’s a real movement towards — how do we get patients engaged, people engaged, as close to them as possible. And I think the phone is a big part of that.
And so, you know, even with what we’re doing in our tech, you know, the further we move along our product roadmap, the closer and closer we get — not only to the phone but to the person — and thinking about technology closer and closer in proximity to a human being. And that allows us — but it also allows other companies out there in the digital health sector that are thinking like this — to have a more nuanced approach to… yeah there’s probably…you know, you could stratify a patient population 100 different ways or, at least. And so, you have to build technology that can flex and pivot and anticipate a little bit about what each of those strata kind of need and want and can react to and respond to.

So I think this is, you know, I don’t know if it was clever idea of yours, but it’s a great way to go back to the AI part of this conversation, which is a AI has a big role in, I think, creating a deeper, more nuanced relationship between a very heterogeneous population and their technology and healthcare.

And I think that’s one of the big things that we’re trying to do, or think about, is incorporating that into being proactive, and looking at that, and taking those inputs that are, you know, from different demographic segments and trying to understand that. And also, obviously, different levels of acuity in a certain disease. You’ve got so many permutations that are going on there. So, I didn’t really answer your question, but I think we’re thinking about that stuff. I mean, might we capture 80 percent, 85 percent of people with what we’re doing? It’s possible. 100 percent, I think, is the great…is the great goal. But I, you know, I think as we get smarter, as technology gets smarter, as technology helps us get smarter, I think we can move the needle along that 80-85 percent to 90 percent and beyond. So, that’s kind of my thought.

Gabriel [00:32:03] Yeah, and it raises the question, I mean, for a socioeconomically disadvantaged population, the time it takes to get to a hospital means not putting in the hours you need into your hourly wage. It means, you know, finding a parking space near a hospital which is never easy. You really…for people living paycheck to paycheck, that’s incredibly hard to take off that time to take care of themselves. And the prospect of extending care through digital tools or telehealth or other types of virtual care opportunities is actually really exciting, because this could actually extend care to those who wouldn’t show up to a hospital for a diabetes screening or for a behavioral health consult. Which sort of is a little bit less required of them because it’s not something acute but would be a good thing to be doing if they could manage their care, because now they could have virtually.

Mark [00:32:56] No, I mean, I think knowing no boundaries — you talked about that in our lunch meeting a little bit — like, you know, hospitals without boundaries I think is where you’re getting care and you’re almost agnostic into what the delivery mechanism of that is.

I mean that that’s probably its own podcast and we’ll probably have some guests on that we can talk a little bit about you know no waiting rooms, no boundaries. Look forward to doing that. [agreement] Yeah. That’d be a lot of fun. So, I think we’re kind of come up on 35 minutes and I think our goal — we didn’t say this at the outset —is to have like 30 to 35 minute, or so, episodes. This is our first one, so forgive us for going over a little bit but, anything else you want to add before we kind of hang up the mic?

Gabriel [00:33:41] It’s been a pleasure chatting with you and I’m looking forward to our future conversations. And to our listeners, thank you for joining us and giving us this 30 minutes.

Mark [00:33:48] Yeah. We really appreciate it. If you enjoyed it, just send us a note. We’d certainly like to hear that. We’ll have something to take notes from, shortly, and look forward to interact with all you and getting your feedback. So, thanks Gabriel for doing this is a lot fun. Thanks. All right. See you.

Mark [00:34:19] Thanks again to everyone who just sat through our inaugural 10xTotalHealth podcast. If you enjoyed the episode, please feel free to drop us a line via our website, 10XTotalPodcast.com. That’s one-zero-ex-total-podcast-dot-com. Or, email either of us at Gabriel@10xTotalPodcast.com, or Mark@10xTotalPodcast.com. We’d love to hear your feedback. Without our listeners we’re just two guys having an interesting conversation, which is pretty much how this whole thing got started anyway. Please stay tuned for upcoming guests across the healthcare delivery, product, and investing spectrum. This should be a really great ride and we hope that you join us. Thanks.