Recorded at the 2026 VIVE conference in Los Angeles, this Beyond Longevity mini-series episode features Mark Zemel, co-founder and CEO of Retia Medical, discussing the company’s hemodynamic monitoring technology that turns continuous bedside physiological signals into real-time clinical insights for high-risk surgery and critical care.

Marc explains Retia’s aim to detect early deterioration, guide diagnosis and therapy, and avoid the unreliability, invasiveness and complexity of older tools, noting deployment in 75 US hospitals and distribution via Medtronic, plus presence in 18 countries.

He highlights FDA clearance for Argos Infinity enterprise software, which extends insights across the hospital and to clinicians’ phones and laptops, and shares a case where rapid detection of falling stroke volume revealed bleeding during AAA repair. The conversation covers workflow-first design, interoperability, cybersecurity, regulatory strategy, and a future path from ICU to broader wards and ultimately wearables for earlier intervention and preventative care.

00:00 Beyond Longevity Intro

00:44 Meet Mark Zeel

01:40 Rata Medical Mission

02:54 Argos Infinity Launch

03:58 Clinicians Want Real Time

05:19 Surgery Near Miss Story

07:20 Why Accuracy Matters

08:42 Why Algorithms Are Hard

10:08 From ICU To Wearables

12:20 Scaling Distribution Globally

13:22 Plug And Play Integration

15:22 Wearables And Data Overload

18:31 Alerts And Clinical Judgment

20:16 AI As Decision Support

21:56 US Versus Europe Markets

23:09 Wearables Beyond EMRs

23:57 Regulation And Cybersecurity

25:07 FDA And AI Pathways

26:50 Clinician Workflow Design

29:39 Bad Data From Friction

31:58 Open Ecosystems Future

33:25 Prevention And Longevity

35:46 Personal Why Wearables Matter

39:24 From ICU To Early Detection

41:44 Rapid Fire And Wrap

Welcome to Beyond Longevity, the podcast that explores not just how we age, but how we can build a longer, healthier future for ourselves.

When you look at the trajectory of healthcare, it is not just about breakthrough science. It is also about the technologies that help clinicians make better decisions in real time, in real world care.

Retia Medical is focused on precisely that.

They develop hemodynamic monitoring technology designed to give clinicians a clearer and more accurate picture of what is happening inside a patient's circulation, helping to support decision making in high risk surgery and critical care. Hi, Mark, thank you so much for joining us today on Beyond Longevity. We're here at the VIVE event.

Please tell us a little bit about yourself, about your company and what you're trying to achieve.

Great. Well, thank you for having me. My name is Mark Zemel. I started Raytia 15 years ago. My co founder is Rama Mukkamala.

He's a professor of bioengineering, University of Pittsburgh.

And we had a vision to transform critical care and high risk surgery by getting more intelligence out of existing data that's already being collected from these patients to help detect early deterioration, guide diagnosis, and then obviously help guide the therapy as well. The older technologies in this space had a lot of problems.

They were unreliable or they were too invasive, and they're very expensive or difficult to use. So we decided we were going to transform that with a more reliable technology. So, fast forward, been doing this now 15 years.

We're in 75 hospitals in the U.S. our technology is sold by Medtronic. And so we started with this vision, right?

And as we've evolved and learned more about this space, we realize it's not just for OR and ICU that getting advanced information about the cardiac performance and the circulation can help a whole range of patients. So last week we got FDA clearance for our new enterprise software product, which we call Argos Infinity.

This allows us to take that information that we're getting at the bedside and then transform it with our algorithms into insights, right, no matter where the patient is in the hospital and make that data accessible to the clinician. On their phone, on their laptop even. They can VPN from home and get access to that information.

Early warning right before the problem is an emergency. So they can act and decide with confidence about what they can do about the situation.

That sounds an incredible advancement. Especially as you know, cardiovascular is one of the growing diseases we have today, and early intervention is vital to a successful outcome.

What has been your response from the commissions?

They're very excited. They were waiting for this clearance actually, to get it out there and get started.

One thing I want to say is we come to vive and you see all these companies with AI and looking at, you know, they have all these risk scores and everything. The doctors and nurses that we talk to, they don't want another risk score. They're in the hospital already.

They know that these patients are sick, right? They, we need more than just, okay, this patient is at risk for deterioration.

They need to know what's happening in real time and what type of problem is occurring. Because there's many different problems. They say it doesn't matter, you know, that these guys might get an acute kidney injury.

And you can tell me that eight hours, like that's why they're in the icu, Right?

But can you tell me, you know, which type of problem it is so that I can then choose from my list of interventions and pick the right one and then tell me, is my intervention working? So the difference between us and those companies that are looking at, say, electronic medical record and pulling out episodic data, right?

And building these AI algorithms is we're taking continuous physiologic signals and then giving you a very responsive algorithm. So it's going to respond immediately to changes in patients condition. We can pick up a cough, right? Like that kind of responsiveness.

I'll tell you an example. We were at a hospital in Ohio and they were doing a AAA repair. Very, very high risk surgery.

Surgeon turned over to talk to the medical student, right?

And my clinical specialist was standing up against the wall and she noticed that our monitor was showing a drop in the patient's blood flow, like a really fast drop. And she started saying, but, but there's. Right. And they're all like, be quiet, be quiet. Like, don't talk when the surgeon's talking.

You never do that. But he knew her. He said, what's the matter? And she said, the stroke volume, that's the amount of blood pumped with each heartbeat is dropping.

And he turned around and then he saw blood pooling in the surgical field. So our monitor picked up that problem before you could even see it in the surgical field. He was very impressed by that.

That's what I talk about when I mean, responsiveness.

That patient ended up coding, and we had to do a mass transfusion protocol, which is like, you know, in trauma, where they, like, throw a lot of blood at somebody and try to keep them alive. Brought the patient back to life, got through rehab, all guided by our monitor.

So when I talk about it, it's like, okay, we detected the problem quickly, right? They immediately, obviously, when it's dropping like that, it's a bleed. It turned out the clip on the renal artery had fallen off. Right.

So that when you have an arterial bleed, it's fast. Right. And that's when seconds matter. They turned that case into a poster presentation and an anesthesiology conference.

And I remember the young anesthesiologist talking about it, and he gave it the whole talk, and then his. His chair was standing in the audience. He looked at him. He looked at him in this way of like, good job.

When I was looking at his body language, he said, is kind of like, that was a gray hair. That was a near miss. And now you're one of us, because we've all been there, right, that you got through that really stressful situation.

You have to understand that these guys, they're like airplane pilots. Like, they don't want to go, like, a near miss with the other plane. They want to stay a mile to five miles away from the next plane.

And in that case, they're trying to stay really right down the center of the road with that patient. They don't want to get on the edge where they might have a complication. They want to get right, you know, as far away from that.

And the monitor's job was to keep them there, to help them understand that they were veering off.

And that's where I really understood, and this was a few years in, how important it was to get that number has to be right, how good our algorithm had to be, that they're going to rely on it to make, really, life and death decisions. Right. Where there's no choice if you get it wrong. That's the difference between that patient surviving and not.

Where we talk about, oh, we're building a business, we're going to make money and all these things. Yeah.

But we got into this for those moments, the critical moments where we can help catch that patient and then make that difference, because at some point, everybody knows the patient's in trouble. Right. The job of our algorithms is to pick it up before it's obvious, right? And that's what gets us out of bed every day.

This is truly revolution error. And it's literally where, like you said, seconds matter, seconds count. Why do you think no other company has come up with an algorithm like that?

This is very hard to do. There are other companies. We have competitors like everybody else. But these are complex nonlinear systems.

It's not like you just throw a bunch of data into an AI machine and it'll pick it out. It doesn't work that way. People think it's like magic and the computer will figure it all out.

There's a lot of physics, there's a lot of physiology, and, and the issue is you're trying to pick up the situations where they're abnormal, you know, whereas a normal sort of like a statistical type of algorithm, you know, will drive everybody to average. I give the analogy of like, if I held up my hand and said, okay, what's an average finger?

Well, if you have, you look at your thumb, your index, your middle, your ring and your, and your, your pinky, like come up with an average, and it won't be representing any of them. Right. That's the differences.

Then you add on top of that, differences in all the different types of patients, the anatomy, you know, the difference between a giant football player and a tiny ballerina. Right. And accommodating all that across the range of not only anatomies, but what patient conditions are they?

Obese, do they have diabetes, Hypertension, you know, they have all these comorbidities to have something reliable across all those different things. It's not an easy thing to do.

No, absolutely not. And yet it's so vital. Where do you see your technology moving to in the future?

Well, we started out at, on the toughest patients, the high risk surgeries and the critically ill, the sickest of the sick in the icu. Right. And our vision here was, let's pick up those events, those really challenging populations, get that algorithm to work.

And as we move forward, we're going to roll out additional algorithms that look at less sick patients, where the incidence of those really serious conditions is less.

So I give the analogy of like, first look for a needle and a pile of needles, then look for a needle in a small haystack, and then eventually will go out of the hospital into the wearables. Same algorithm, foundation. Right. But picking up these problems at home.

So I give the analogy of women, when they have a heart attack, it might show up as they, they have nausea or indigestions. Very non specific.

Well, if I told you, oh, put on this wearable device and that'll tell you, okay, is it really indigestion or maybe it's something else and you need to go to the hospital, well, wouldn't that be of value, right, that you could decide what to do?

Because who wants to go and sit the ER for eight hours waiting to be seen to decide whether, okay, it was just a tummy ache or you're, you know, you're in trouble. Right. A lot of people wrestle with that. We're going to help pick up those problems. Right. And then route you to the appropriate site of care. Right.

And then intervene before it's too late.

So you can start to think about, not just, we talk about negating and negative, where it's like, okay, we're going to prevent the disaster like a firefighter or you know, one of these cops that's like, you know, casing the area and preventing the crime. Yeah, we all like that. But we're also uncovering opportunities for interventions which create revenue for the hospital.

And we all know, right, hospital has to make money in order to deliver care. Right.

So if we can find those opportunities for those life saving interventions, because we were screening everybody, we're both improving patient safety and, and outcomes and also doing it in a financially sustainable way for that hospital and the healthcare system.

How much in money terms in life saved do you think does your technology contribute to the clinical field?

Well, right now we've probably been used in maybe 35,000 patients and that is growing by tens and thousands over the past year as we've accelerated our ramp. And as far as I'm concerned, the broad applicability of these algorithms will easily go into the millions.

You know, we just have to get all the distribution channels right, which is my job, right. Get it in with the right people. And that's why we're at vive, right, to build those partnerships.

And you're currently just selling it within this U.S. is that.

No, we're, we're in 18 countries in the U.S. we're sold by Medtronic. Outside the U.S. we have a variety of distributors. That's for our standalone monitor, our new product which we're launching here.

Obviously we're building those, those ecosystem partners right now.

Tell us a bit more about the integrated systems.

Yeah, so there are a lot of companies that are providing what I call the digital infrastructure for hospitals. Think of like a digital or, or a tele ICU or other sets of systems where hospitals recognize that we can't just rely on Standalone boxes.

We need to have that connectivity to provide that surveillance across every bed. Right. So that we don't have to wait for the right person to see the patient at the right time. Right. We need to make that data accessible.

So there's several of these companies that do this, both small and large.

And so what we've designed is architecture of our software so that it can essentially be a cut and paste into those systems without having to reprogram and create all these custom drivers and everything. So that thankfully, I have a great engineering team that's been able to do all that.

And I don't want to get too technical here, but the point is that we fit into that ecosystem without telling the hospital, okay, we can do this, but you got to spend five years in it hell, to actually implement it. That's not happening. It's gotta be plug and play into their existing infrastructure.

It makes it sound very scalable.

Yeah, well, that's the point, right? Yeah. We're not in it to help the one patient. Right. We're in it to help the whole, you know, gamut of patients that the hospital has to. Has to manage.

And we saw this with COVID Right. We can't make enough skilled doctors and nurses fast enough. Right.

And the populations are getting older, you're showing up sicker, they're all overweight, and they all have diabetes and all these problems. And, you know, that's where monitoring and these technological advances play a role to help you allocate your, you know, precious and limited staff.

Right. In a more efficient manner. Right. Because nobody wants to get sued because, oh, you didn't catch this. Right? That's a real problem.

And moving into wearables, how do you see that working? Is it going to be connected directly to a clinician, or does it rely on the patient or the wearer themselves to be proactive?

Thankfully, that's not our problem per se. What I see is the patients are going to drive it. They're going to demand that.

I mean, people will show up to their doctor and say, look, here's my data. What do you think? Right. I told my mom, okay, buy an Apple watch and start tracking. Right. Like everybody.

And so that's, I think, going to be the push before. The doctors didn't want that data. They didn't want the liability. That, that, that ship has sailed. It's. They're coming with it.

And the leading clinicians are now partnering with these companies and saying, okay, tell me what you know. So our job really is to just fit into that ecosystem. In a way that. Such that we can get the data visible to the right people and not overwhelm them.

Because that's the other issue is people talk about data lake or data warehouse.

A lake is like this big, like garbage dump of everything and you need to make it structured and digestible because people are going to get information overload very quickly.

So the beauty of what we do is when I come back to the foundation, it's not just detecting the problem, but helping you figure out what type of problem and then guiding that treatment. Right. That's a value add that will be useful no matter the site of care.

How do you discern what data is valuable and really relevant?

If you talk to folks who are experienced clinicians, they say, oh, you don't need this. This is for a patient who's in the ICU until it isn't. Right. Like you don't need it until you, you do. Right. Like when everybody's fine.

I mean, I don't need to know my blood oxygen content 24 7. Right. I need to know when it's abnormal. Right. Well, how do you do that?

You can't just like put on the ring or the watch when you think you're in trouble. The movie's already half over.

So in my view, this just, just be collecting it passively in the background and you never look at it until it's abnormal. And then you. When what we happen, we. We see this in, in the hospital is they're not looking, not looking.

And then when they see something, oh boy, they zoom in and they look at everything then. And they study it like a zoom lens. Right? Right. You don't need it. And then when you want to zoom in, you go to the full extent. Right.

That's what they do with our data. Just sit there quietly collecting and then it's there when you need it.

The data that the clinicians get when there is an emergency, right. Is it tailored down and focused on where the fire is burning or do they get all the data you're going to themselves need to analyze?

You are going to get an early warning of a patient is deteriorating. Right.

You're going to get that warning on that individual patient so you don't have to look at all thousand patients, just look at the 10 that are in trouble. Right. Or the one that's in trouble, depending on the site of care. Right.

So that's going to surface to you automatically so you don't have to sift through all of it. Once we give you that warning. Right. Then we're giving you all the information that led us to that decision. Right.

And then you can start to think about, okay, what's going on here. And the folks that know, they look at our numbers immediately and they know exactly what to do, not what the problem is. Right.

They're able to tic tac toe, figure out what's going on fairly rapidly.

The clinician's input is still vice.

People talk about AI replacing clinicians, and we've taken the approach, we're going to augment their knowledge and experience. We're never going to sit there and say, okay, we're going to do what you do. We can't. You know, the most we can do is give you information. Right.

We're not going to do a therapy or some kind of other diagnostic decision because ultimately we are still a piece of a larger puzzle. Right. There's a lot of other data. You know, your, your blood work, you know, like, you know, how's your breathing?

Like we're giving you, how is the cardiovascular system performing?

It's an important piece, but it's not the only piece to your health, you know, so we've taken the view we're going to give you that information when that piece of the puzzle is not fitting, not working normally, and then you can decide, based on what else is going on, what to do about it.

I think that's very important to clarify because a lot of patients are scared that their health plan is determined by AI and not a real person. So I'm very happy that you made it clear that the only information the doctor gets are the results or the information, but not what to do about it.

Yeah, I mean, look, at some level, if we're starting to detect problems, right? You look at the Apple watch for hypertension, it is now telling you, oh, you might want to get your blood pressure checked.

In theory, we could do that same thing, right? When we start to see you going south, we say, okay, something's not right here, what's going on, Right. We could prompt investigation.

And then that means, you know, maybe there's some back and forth between our system and the patient. So saying, oh, it's fine, I just did this. Right? Or maybe we're telling you, oh, you know, you better get checked out. Right?

But that's about as far as we're going to go. Because making that kind of decision, it's the regulatory bar. You need an MD for that.

You know, our software is not replacing the years of training and experience. We're not up to that. Not just our company, as A society. And I don't think we want to be.

You know, it's one thing to have a self driving car with like clear lanes in Phoenix or la. It's not driving through snow. Right. We're not even, we're not approaching that with this kind of technology.

No, I think that's very important to, to clarify that it's a support system.

Yeah.

Rather than an autonomous system.

Exactly.

How do you see the markets being different between the US and Europe? Do you see any sort of resistance to all these new technologies in one area more than another?

Or, you know, it's, it's interesting because they're very different dynamics in our space. Actually, Europe is in some ways ahead of the US in terms of its knowledge and use of this type of information.

But I would say the US is more ahead in terms of its IT infrastructure. The data is more well connected here. There are some countries in Europe that are better than others in that realm.

But that is going to be a challenge as we expand globally to build partnerships with those folks in places where they're still recording records on pen and paper. Right. I can't help you with that. So it's going to depend, country by country where this technology is going to play better.

But we saw this in even some of the lesser developed countries where they bypass all these expensive legacy systems when they adopted cell phones. And it's like, okay, now we can even use it to transfer money. There might be a similar type of bypassing happening from the consumer side.

And we know this, people are going to buy these connected devices and they're going to be self monitoring and that's going to go past this legacy infrastructure of EMR data. And that's to our benefit because that's kind of a lot of the real time signals that we're able to analyze.

Absolutely. And in a way it cuts out the middleman.

Yeah, yeah, exactly. And look, that middleman of data from the EMR is really helpful.

But EMRs were built for billing, you know, they weren't built for really aiding detection, diagnosis and therapy. Right. So, so the beauty of this is what are these wearables for exactly?

These issues of screening and making sure that you're doing okay and optimizing your health.

Do you struggle a lot with regulatory hurdles?

We've been very lucky.

We have some great regulatory consultants and advisors, not just for standard FDA or CE mark, but also in the realm of cybersecurity, which is a huge issue, you know, maintaining that data privacy. You know, you don't have your infrastructure Built for that. Forget it. I had a podcast the other day, we were talking about this.

If you have a situation where somebody got into your monitor or your algorithm and they monkeyed with it and you gave the wrong reading on that patient, your reputation is gone. You don't get a second chance. So we've been very lucky. We've had some great experts in both of those areas to help guide us through this.

And I would say for those folks listening, if you think you're going to argue with the fda, why. Right. Why do that? Right. Especially for small businesses, they want to encourage innovation. So just tell them what you're trying to do.

Be open about it. Right. And then work with them to make it fit in the right pathway that they'll get you to the destination that you both want.

Do you think the FDA and you European equivalents are more open and knowledgeable about these AI help technologies?

Yeah.

It's funny, because FDA is built on this whole precedent kind of approach where it's like, okay, this is equivalent to what we did before, but it's never really equivalent. It's just sort of mostly equivalent. And then we add on top of that, and we add, this is like, sort of like foundation building also.

This kind of like, okay, we're going to build on precedent, but we're also going to issue guidances. So, you know, you have to meet these standards. So they're kind of triangulating across both of those. And as these technologies have evolved. Right.

It's difficult to keep up with the pace of the technology for the regulators, the people that are developing these technologies are the true world experts in this space. And the regulatory folks have to kind of say, okay, how do these things create new risks, you know, or how do they.

How do I prove that these things are effective, that they're safe? Right. These are the. The classic things that you're looking at. And so I think it's a challenge for them.

But I would say I've been pleased when I see that they continue to think about, okay, we got to figure out a way to.

To enable these technologies and work with these technology providers and not just put our head in the sand and say, okay, either put up a big stop sign or let it be the wild west.

I think they're taking a nice middle ground, middle path in there as opposed to some other areas in other industries where they're not even doing anything. And it's just a whole wild west.

Now that you've rolled out your technology in clinics and it's gone live with real patients and, and clinician you use in a daily. Have you had any direct feedback from the clinicians as to what they would like to see in the future? Maybe added or amended?

Yeah, we solicit feedback all the time. And the beauty of software is you're always iterating and the thing that they want is for you to really understand their clinical workflow.

So a lot of the effort we put in is, okay, how do we make this easier for you?

Reduce the friction, reduce the number of clicks, the number of nested menus and other ways for you to get the information that you need and nothing more. So if you think about it, people have, you know, an iPhone in their pocket.

Nobody reads the manual anymore, they don't need a, a clinical consultant standing next to them coaching them. But historically that's been the medtech model.

These expert clinical specialists and reps who are sitting there and telling you exactly what tool to use, how to turn it, how to, how to position it and all that. And that's great, right? But I look at it and say, if you have a really good design, it should be very obvious.

And so that involves a lot of listening, a lot of going back and forth and saying, okay, what if we do it this way? And it's kind of like, you know, you could say obsessive for, for us, but the difference is the number of complaints we get. Right.

And the number of problems we have are way lower. We track our net promoter score. How likely are our customers to recommend our product? And the typical med tech is in the teens.

You know, on a scale of zero to a hundred, we're in the high eighties. And really I'd like to get to 95, which is consumer level. Right. So we've got some work, right?

I don't wanna say we're perfect, but people hug our devices, the nurses will hug our monitor because it's empowering them to see around corners and see problems before they're even aware. And they're very passionate about it. They're, they're patient advocates. So that tells me we're on the right track.

No, absolutely.

If, if nurses and doctors are grateful for your help and they see it as a help and not a hindrance because a lot of work that they do gets lost in, you know, bureaucracy, administrative work. And if you just add another hurdle to their work, helpful as it may be, they will not be grateful. So it's very encouraging to hear that.

They're so happy with using the device and it should be intuitive because doctors are very Talented people, but not very often, you know, technologically that inclined.

So one of my friends works in an ICU and they got some algorithm handed down from on high. It said, okay, we're all going to use this thing and it's one of these machine learning AI things, right?

And then now when you deploy them, they can learn while they've been deployed and they train and they're supposed to get better, Right? Well, here's the problem.

He said, nobody talked to me, meaning him, about what this is going to mean for my nurses who now have to do five more things than they normally do. And I'm going to put all these alarms and other things to zero and infinity because I don't want to deal with this. Right.

And what does that mean for their algorithm? They're going to be training on bad data. So we all preach to the choir and we say make it fit into the workflow. But it's not just a nice to have.

You're trying to make your algorithms work, you better make sure that you're getting good data because your users are, are using it in the way it was intended. And that company, they won't know that, right? They won't realize it. And then their algorithm's gonna spin off into nothingness. That's the problem.

It's gotta fit with what they do. And that means a lot of going out and talking to people. Get out of that cubicle, your office and go and sit down with people.

And when you think you've talked to enough, talk to another 30, another hundred more.

Absolutely.

It's the same, you know, once you move into the wearable era, it's, you need the data from John Smith on the street using a daily, and if it's too complicated or too inconvenient, they will not use it.

So it's very important, I think that you've realized that early on that it needs to be easy to use and people should be happy to use it and want to use it because it's sort of a two way street.

Yeah, bad data is bad data. Right. And where does bad data come from?

It can be also come from humans, not just passive data, because all those things go into the context around how that data was collected. If you're not sitting there regularly in that context, you won't even realize the bad data that you're collecting.

Where do you see the market as a whole moving in the next five years or ten years maybe?

Well, I think this path that we're on is going to be ever more data ever More insights. And there's going to be, I hope, a broader adoption as opposed to this segregation between the haves and the have nots.

Because I worry about that, especially in rural health. Great. I don't even have the funding to adopt all this stuff. I think that's going to be an issue. But.

But I have faith in what I call the engineering ingenuity that as we move forward, data is going to become less and less expensive.

Like historically, if you want to data with some expensive box with an expensive disposable because people were metering the data, think of like, you know, when Xerox came out with the photocopier and they charged per copy. Right. And now you just buy a ream of paper and you feed it into the machine. It's unlimited. Data's going to go in that same direction.

And the companies that understand that and are collaborative and create open ecosystems, those are the ones that are going to move forward.

And the ones that are in this, like, okay, we're going to ration data and we're going to try to own it or trying to like, you know, only give it to the premium and whatever. They're going to get less and less of the market. So that's how I think things are going to move forward.

Yes, I think it cannot be a siloed approach.

Exactly.

Doesn't help anyone.

Do you think making that I would call sort of preventative care, be more available to the person on the street, do you think that's going to significantly contribute to general health and well being and longevity as a whole?

I'd like to think so. The way I think about it is I grew up in the era where a seatbelt wasn't mandatory in the backseat, right?

And so now it's like, okay, people don't even think twice. They wear it, right? Then we have the airbags, we have the analog brakes, now we have the backup cameras.

All these things, they come out in the premium car and then they slowly diffuse and they become the standard of care. Data is the new safety innovation. Right. And these things are going to diffuse throughout the marketplace. And yes. So whereas before, right.

Think of surgery like there was a risk of dying 50 years ago because of anesthesia. That risk is really low now.

Now it's about, okay, grandpa went to surgery and he came back and he's not all there anymore mentally because he had delirium. We're going to continue to whack down these problems one by one.

And that will mean that these patients are going to be able to survive situations that couldn't survive or we're going to catch them sooner, does it mean that, you know, we're going to suddenly all live longer? It's a piece of the larger puzzle. Like, if you were going to still take risks with your health, right.

There's nothing information is going to do about that. Right.

But for those people that want to actually invest in their healthcare, well, yes, I think it's going to minimize those woulda, coulda, shouldas if we only knew, because that's the, that's, that's the whole point.

A lot of people, fine, they don't care. You will never convert them. That's okay. But I think that a large majority wants to live better, live healthier, live longer.

But a lot of them don't know how to do it other than maybe, you know, eat vegetables and make sure their protein intake is, is, you know, up to scratch. But that is just not enough.

And the beauty of this is now we're going to pick up problems that you weren't even aware of. And to have that capability, that's what's exciting to me. I'll give you a story. I got into this because my father died of a sudden cardiac arrest.

And this was. He was on a biking trip, it was 20 some odd years ago, and he went up the hill and that was it. And he didn't have the wearable.

He didn't know that he was in trouble. Right. And all of a sudden, you know, they're in the middle of nowhere and the ambulance took 45 minutes to get there.

And, and, you know, there's nothing more we can do. We have technologies now, right, where we can track you and then we can tell you. Okay, ease up, slow down. You gotta take a breather here.

Don't push through it and, you know, blow a gasket. Effectively, what he did, I assume, would we have been able to prevent it? Who knows? But we shouldn't give up from trying, right?

We, it's not our job necessarily to solve every problem, but can we make it such that we catch some of them? Right. And then it's that person's father or grandfather that we helped. That's, that's why we do this.

No, no, absolutely. And a lot of diseases start 20.

Years ago, as always.

Well, I brought. If your dad would have had a wearable maybe 10, 20 years earlier, it would have said, ooh, you know, you do have repentance, you do have some issues.

And he could have looked into it.

Exactly. And look, I know how old he Was, I know how old I'm going to be. And it's like, okay, we measure our life by the lives of our parents, right?

And it's like, okay, maybe I'm going to be a little more careful, right? And I'm going to watch. He didn't know, right? How do you know?

Now you can know.

That's the point.

That's the beauty of it, right?

You know, you couldn't before, but now we're sort of at the cusp of being able to maybe not look into the future, but certainly make it into a better future, a healthier future.

Ten years ago, the Oura ring came out, right? And I was like, who needs to. Who needs this? I was wrong.

And now they were all toys back then, and now they get more and more sophisticated, and over time, we're going to learn more and more about the human body. And is it normal? Is it not normal? And I think the beauty of this is there are people who are like, I don't want to know, right?

I don't want to be in that world. But I think more and more people are like, you know what? It's only going to tell me if something's wrong. I'll wear it. And you know what? I don't.

I don't need to think about it. But I know it's there watching over me. And that's the beauty of this.

Now that it's actually going to tell me, and I don't have to sit there and obsess over it. I'm not going to, you know, get all crazed about whether I'm perfect, right, because your body is very robust.

These technologies are going to tell you, okay, you need to worry or you don't need to worry, as opposed to, okay, do I trust it? It's really accurate.

You know, I don't want to have all these situations where I go to the doctor unnecessarily because it was giving me, you know, bad information. That's where it was 10 years ago, right? But now.

And in the next 10 years, it's going to be so precise, and you'd be like, wow, it'd be, like, ridiculous for me not to watch myself.

And I think the people that saying, I'd rather not know, refer to the fact that they'd rather not know when it's too late. No, we might not want to know. We have cancer stage four, and there's nothing to be done.

But I think most of us would want to know if we have cancer at stage one. And a lot can Be dark.

Exactly. And that's true in cancer, that's true in cardiovascular disease.

Absolutely. No, I think it's true.

A very important field for anyone, be it the critically ill or the very healthy young people, to see where their trajectory is going and what they can actively do to help themselves. Because the whole idea, I guess even for you is even though of course your business is in hospital, helping the critically ill today. There you go.

Exactly. Is to avoid getting there, right.

Cutting that number down, but obviously pivoting your business to the general population with wearables and other data information that is helpful to them.

That's right. That's right. And I think about this a lot because by the time they get to the OR or the icu, right, we already know a lot about what's going on.

How do we know about it? So you can decide to go down that path so we can intervene in those early stages. Right. Think of cardiogenic shock, a cardiac issue, right.

There are lots of valves, heart pumps, other implants that are coming on the market right now. But if we don't catch the problem soon enough, right, it doesn't matter. And you can pick up these signs 12, 24, 48 hours ahead of time. Right.

That's the beauty of this software, that we're going to pick up these things so that then we can have that conversation, if only, right. That we, we caught it in time. Right. Your, your analogy of the stage one. It's true. In all sorts of conditions.

Wow, what a fascinating talk. I love this, you know, because it's so futuristic and yet it's here today.

Right.

I, when I think about running a company like this, you know, you have your, what we're doing right now, but you have to have that vision because what is software without the roadmap, right? Where do you go? Because people expect it. And the beauty is there's plenty more to do. That's what gets us going every day.

And you know, we're excited to be part of this journey and to make that input.

We are all very grateful to you to be on that journey and more to come on that topple. I can't wait. So, Mark, before I finish, we always have five raffish fire round questions.

What's the single best piece of advice you would give your younger self?

Have faith.

Name one habit everyone should adopt for a longer, healthier life.

I would say eat right.

If you weren't in the, let's call it longevity science or you know, life extending science, what career would you have chosen?

I can't imagine doing anything different at this point.

What microdose habits Sort of a five minute routine or small daily action yields outsized longevity benefits.

Oh, exercise even a few minutes every morning.

Last but not least, what's the craziest longevity myth you've encountered? And is there any truth to it?

Oh my gosh. I. I don't know. There's so many different weird like hacks that people have apple cider vinegar at night or like all these wacko things.

You know, it's the basics of blocking and tackling. You don't have to be perfect, right, to live a long life.

A lot of it's still genetics, but, you know, take care of this body that you were given, right? And it'll take care of you.

We can only do the best we can. Thank you so much.

In this episode, recorded live at Vive in Los Angeles, we talked about the clinical problem Retia is trying to solve, why better cardiovascular intelligence matters so much at the bedside, and what it takes to build technology that can genuinely improve care when timing and precision matter most. If you enjoyed this episode of Beyond Longevity, please follow rate and share it. Thank you very much for listening.