Tech Brew Ride Home - (Bonus) Calmwave Profile

Episode Date: May 26, 2025

Find out more about Calmwave at Calmwave.ai. The case study we mention is here: https://catalyst.wellstar.org/casestudies/calmwave/ Learn more about your ad choices. Visit megaphone.fm/adchoices

On April 4th, 2023, around 2 in the morning, a man was found stabbed multiple times on a sidewalk in downtown San Francisco. Hey, who did this to you? What happened next turned the story into a political firestorm. Reports have identified the victim as Bob Lee, the founder of Cash App. From Bloomberg Podcasts, this is Foundering, the Killing of Bob Lee, beginning April 16. Welcome to another weekend bonus episode of the TechMeme Right Home podcast. This is actually going to be a holiday episode, I believe, Memorial Day. I'm your host, as always, Brian McCullough.

This is a portfolio profile episode. We haven't done one of these in a few months. We are going to be talking about AI. We're going to be talking about AI in healthcare. We're going to talk to Afir Ronan, who is the founder of Calm Wave, Comwave.AI. Thanks for coming on the show. Brian, thanks so much.

Honored to be here. So let's get this out of the way quickly. Give me the elevator pitch for what Calmwave does, and then I'm going to tell you why I was so excited to invest in it. All right. Comwave is a company that is building an intelligent hospital operations platform. What does that mean? That means that we fuse the ice ice.

silos of data that are everywhere in hospital systems and use those to do lots of things. We're starting with finally fixing the problem of alarm fatigue in ICU. Hopefully you haven't encountered that problem yourself, but if you haven't, you know, patients today encounter between 400 and 800 audible alarms per day in an ICU. And remember, you're in an ICU because you're arguably in the worst state that you've ever been in your life and what you want to do is sleep and rest and recover. And you can't because every one to two seconds, something is beeping next to your ear. And also, if you are a medical professional, you are in that ICU trying to triage, trying to handle what's going on.

Absolutely. And there's beeping going on in all the rooms all around you and all that. Yeah. I mean, this causes issues where nurses wake up in the middle of the night hearing phantom beeps. That's a very common occurrence. You know, their microwave beeping will trigger them. There's a reason why we have such an incredible challenge with nurse burnout. It's not just the overwhelming work.

It's also the really tough environment that they're forced to work in. And so what we're doing is, yeah, go ahead. I was going to say, so you're using AI to essentially stop the cacophony, right? And explain to me on a basic level how you're doing that. and then how AI fits into that? Yeah, so here's kind of the foundational problem. The foundational problem is you've got these bedside monitors that monitor patient's vital signs, right?

You see a patient, they're lying back on the bed, there's a bunch of sensors on them, and the clinicians are watching the vital signs, heart rate, blood pressure, respiration rate, oxygen saturation, and so on and so forth, because that's giving them an indication of how that person's doing. And then they're also applying these really powerful vaso-active medications to change those vitals

based on what's needed. Increase heart rate, lower heart rate. But, you know, same thing with blood pressure and so on. And the problem is that these devices are deployed with a set of default alarm limits, right? So an alarm limit is, I'm looking at this signal, let's say heart rate. Anything between a low heart rate of 60 and a high heart rate of 120 generates an alarm. That's an example of a default. The problem, though, is you can't apply, you know, the same defaults to the same populations.

or to different populations. Because the problem is, everybody's different. It's not just the populations. It's also the individuals. So, for example, in egregious cases, I'm going to deploy this heart rate, which is set up for adults at a pediatric ICU or a neonatal ICU. They, by their very nature, have much faster heart rates, and that's okay. But if you apply the same defaults, you have a cacophony of noise. And the same thing applies if you're looking at like a cardiac care per unit versus a different type of unit.

people are different. And so today, the bedside monitors have the ability to be updated in terms of their alarm limits, but there's no guidance whatsoever provided to clinicians of what those changes should be. And that causes everyone to say, hot potato, I don't want to test this, because if I make a mistake based on my intuition alone, not hard data, then that could be bad. The outcome could be I lose my license or God forbid somebody dies. So what Calm Wave is doing is we are, the first company to have ever come in and said, look, you have all of this data here. We're going to unify this data so that we are showing you not just the vitals, but also the vaso-active medications and titrations. We use all of that information to say, this patient is now stable. This is a great time to make a small adjustment to the alarm limits, as opposed to the status

quo, which is they make huge changes to the alarm limits to suppress the noise because the patient's in distress and they're trying to fix the problem. And our system is working in the opposite way. So again, to be very basic about this, there's a ton of machines pinging and like just from a ambient sort of sense of it, like I don't know what ping is more important than another ping or whatever. And so essentially this is, again, to use that, we're triaging this, figuring out what is more important. One ping is more important than another or less important than another. But what do you doing specifically, what are you putting in the hospital, in the ICU that does this? Yeah. So what we're doing is we're giving them a unique way of looking at the data. So today,

if they want to look at a patient's vitals in sort of near real time fashion, they look at the bedside monitor. If they want to look at the medications associated with that patient, they have to go to the EHR, which is electronic health records, and look at a number of different screens that have tables to figure out what the medications are and then try to align them all in their heads with what the vitals are doing. Our platform is unique because we integrate with both of those systems, the middleware that's aggregating all the high frequency vitals and the EHR that has medications, interventions, labs, notes. And because of that data fusion, we're showing them their medications overlaid on top of the vitals. So they can just hover over a medication. You

administer this medication at this rate, this dosage at the last five minutes, this is the impact that it has on the vitals. And they can see that all at once. So that's causing them a massively improved ability to handle that cognitive load because it's all in one place. And how is AI specifically playing into this in the sense of do I need to install this and like it's looking for patterns after over time? Yeah. Yeah. Yeah. So our system is built so that everything is focused on what we call transparent AI, which is full transparency to everything, right? There's no LLM that could hallucinate. People will not accept that in critical care. So for us, it's all about machine learning. It's all about math and statistics because that's

the incredible validation that is needed for them to approve something like this, where you're providing recommendations for them to adjust the alarm limits on the bedside monitors, which are where they get the, you know, understanding what's happening with the patient. So, I mean, one way to kind of characterize what we're doing with respect to AI is when we are, one of our charter customers is well-starred. And we just came out with it. They just published a case study today about the efficacy of what we're doing with them in the cardiac care unit. And probably two weeks before we did the contract with the entire hospital system, they asked us to fly out to Atlanta in order to do a presentation from the chief medical officer, chief medical informatics office. officers, two chief nursing officers, and their staff. So it was a big meeting. What they told us

ahead of time that, you know, the CMIO was incredibly sharp individual, but also if he didn't like what you're saying, then you're done. He was that gatekeeper. So I said, all right, we're going in. So we go in, we start this presentation. It's like a two-hour presentation with 30 people in the room. And we go through our value prop, we go through all the pieces, and we start getting into the math and the science of what we're doing. And what was interesting there is we were probably nine minutes into that presentation. And he stands up and says, I'm going to stop you guys right here. And I was like, you know, heart dropping. And then he says, you guys shouldn't even call yourself AI. This is math and statistics. I understand exactly what you're doing. Well done. This is

truly innovative. And too weeks later, we got the contract. So that was like an incredible validation of the approach of transparency, right? Because they're relying on this mission critical recommendations in order to adjust the limits. And if the clinicians don't understand how you got there, then that's a problem. This is an aside. But I've been talking to people recently about this concept of, you know, having been in a space where machine learning and AI was something that maybe you have been working on. People have been working on for years, even decades.

And then it was an obscure sort of thing that was always next down the road or whatever. But then all of a sudden we're in this moment where you don't have to sell people on how AI can change things anymore. Like the guy standing up and saying, oh, stop it. I get it. This is, this is transformative. Are you getting that sense in terms of like this technology applying to what you're doing? So this case study will certainly help that. But, you know, until we got to the point of this objective success, there's a lot of hesitancy, right, because of what we're talking about. Like, you know, my background is enterprise IT operations, certainly mission critical, but this is much more so, right? Because now you're not talking about an algae causing millions of dollars of losses per hour. You're talking about patient outcomes, right? And our goal is to objectively improve patient outcomes as a result. of nurses having better tools, doctors having better tools. So in terms of like as an example of how

we use what we call our transparent AI, which is all machine learning, is we're ingesting these high frequency vitals, right? So we're getting all the vitals every 30 seconds. We're pulling in the interventions, the medications, the labs, the notes, the nurse to patient assignments. We're fusing that and time aligning everything in our calm wave common signal format. So whatever format is in the outside world, on the inside, it's all beautiful, contextual data normalized and set up for data science. And our data scientists come from aerospace, right? Like the core of my data science comes from, you know, Boeing research and development, where they're focused on math and statistics to do all sorts of really mission critical, you know,

projects. And so that was really helpful in bridging these two worlds because you're bringing in that domain knowledge about the efficacy and importance of being able to understand how you got to these results into healthcare, where in their context, you know, they're not provided a lot of this type of transparency in healthcare. They're not really accustomed to that. And that's what's so important about what we're doing because we're all about, we want to show you everything. Well, so I promise that I wanted to say why we were so excited to. to invest in this. And Chris and I both, Chris was not able to make it today. But we've said before

that our original thesis when we started investing in AI was what we called AI variatals, which now people have come to say is the value of AI is going to accrue to the application layer. But in our original theory was this is just a new flavor of compute that can be applied to any use case. any, any, um, uh, niche, any, uh, whatever. And, and so again, like this is what, what you're describing is a new type of compute that wasn't possible before this technology turned up to do something, uh, sort of an awareness and a, a sort of, um, observational level that wasn't available before this technology came up. And so, uh, sorry, I'm going on a bit long, but. Okay.

People like to, people are saying like AI is a buzzword and it's like sprinkling pixie dust on things. But no, it's just a new type of compute that can be applied to a million different things. And this is one of my favorite examples of what we've invested in that like proves that thesis. Well, thanks, Brian. That's very kind. And yeah, I mean, we agree. Like one of the things that's that's really important for people to understand is that AI is a label. and there's a lot of things in that label, right? Everybody thinks chat GPT is AI,

but there's machine learning, there's, you know, there's so many different flavors of it, but it's all about how do we use math and science to do what we want to do, right? In a provable fashion. And the thing for us, it's so exciting is like, you know, are going in and solving the curto unsolvable problem with alarm fatigue, right? Like this has existed for 50 years and no one solved it.

Our solving that problem is so that we get access to the data. and we get access to the operations of the hospital. But we're also building what we call incident patterns. And for us, one of the ways that we understand when a patient is stable, because that's the only time we provide these safe updates to the alarm limits, is that we create what we call incidents. And incidents are containers of context of vital sign abnormalities. So what do I mean by that?

You have patients that, you know, there's the alarm limits, but there's stuff going on in between the alarm limits that are still valid and things that you should be looking at. So there's no beeps, but there's elevated heart rate and lowered blood pressure and whatever else is going on. So what's exciting about our incidents is that we use a series of mathematical engines where we look at the signals that are happening and a signal is a vital sign, right? So we look at heart rate and we're seeing, you know, the heart rate starting to rise.

So we're looking at classical statistical methods. So we're looking at velocity and bias and trending and all of these things. things that are known good, right? And then we're looking at what's happening in the signal. We're classifying each signal into a set of different regimes. This is super stable. This is stable. This is unstable. And then we're doing that for all the vitals at the same time. So that allows us to understand, hey, something's happening with this patient. It started with one vital. Now two minutes later, it's two vitals. Four minutes later, now five vitals are all causing interesting things that are happening. And what's cool about that is,

is we are able to show now that as a result of nurses making updates based on our recommendations, they're receiving earlier warning of patient degradation. So, for example, let's say the default heart rate limit is 140 beats per minute. Our system will go in and say, hey, based on the condition and physiology of this patient, it should be set at 125. And here are the five safety checks that all must be true before we'll ever surface that recommendation. So the nurses get to see that. So now they have that validation that, yes, this is a safe opportunity to make this change.

They make the change. An hour later, they get nine minutes of earlier warning of a patient degradation. And when you're in the ICU, nine minutes is literally someone's lifetime, right? Like, you can save someone's life for that nine minutes. But then when we look at where our incident formed for that specific incident, it was 47 minutes before when we first started catching it. So we're not providing alerts today. So just two quick terms. Alerts are what you send to people to let them know there's a problem.

Alarms are what you get from the things, whether that's hardware or software. And whenever you see a one-to-one association between alarms and alerts, that means you have a problem because there's always way more alarms than there should be alerts. And that is the case in healthcare today. It was the case in enterprise IT, but we fix that. And just real quick before I move on to something else, this works essentially across the universe of B.B. devices from various vendors. And so like I can plug this into an ICU and it doesn't matter if it came from, I don't know, Phillips or whatever.

Like this can triage all sorts of things. So what we do is we integrate with the existing silos of data. So we don't actually monitor the device of themselves. That's not our, that's not our area. What we do is we integrate with the EHR, which is all the medical records of all the patients and all the clinicians in the hospital. and the middleware that's doing that aggregation. Right. So, like, I want to talk to Phillips Intelbridge, which is pulling in the data from all 3,500 BetSide monitors in the system, right?

Or Phillips capsule, which is pulling in the ventilator data, the infusion pumps and all of that. So to us, everything's a signal. And so as long as they have middleware that we can integrate with and pull in more and more of this data to fuse together into our common format, we just get smarter and smarter and provide more and more value every time we do that. The system's actually getting smarter, the more data you feed it. Yeah, exactly. And one of the things, like, as an example, the things that we're learning in healthcare are really fascinating. Like, for example, data is deleted on a rolling 30-day basis. Vital sign data, because nobody has anything to do with it.

And for us, we're looking at it and we're like, this is incredible data that's going away forever. And so for us, when we fuse these things, you know, we de-identified the data. our platform de-identifies right at ingress. And as a result, we have this incredible amount of fascinating de-identified data, the most, the richest patient context that's ever been put together because of the fact that we have the high-frequency vitals time aligned with everything else. Because up until this point, people just looked at the EHR. They didn't look at fusion of the vital signs because now you can see what was the impact

of applying epinephrine to this patient on their vitals during this time. And you can't see that just by looking at the HR. So that's novel. And that's really interesting for the future, where we can start really understanding what's happening. You mentioned a little bit about your background. So let's dive into that. Yeah. Tell me how you came to, give me your career a little bit, and then tell me how we got to Calm Wave. Yeah, so I've been building technology companies for 25 years now. Comway is my sixth startup. My first startup was all the way back in the early days of the internet, and I know I'm dating myself, but that was one of the first commercial internet backbones called Internap.

And Internap at the time was interesting because we built a backbone that overlaid on top of the physical backbones software. So that was our early use of AI back then, and this was in the mid-90s. And then I sold my last company to PagerDuty. PageDuty is the world's largest IT operations orchestration company, so they're in charge of helping Fortune 100s marshal their resources, so they don't lose millions of dollars per hour when there is an outage. At time of acquisition, PagerDuty was focused on guaranteed delivery alerting, so they would make sure that you could get access to the right people who were on call

to solve the problem and escalate up to the CEO. What my company was doing was handling the high-frequency alarms from big IT management systems, normalizing them into a common format, creating incidents. This may sound familiar because I'm also a pattern. We're now applying an enterprise IT operations pattern that works at scale for Fortune 100s, and now we're applying it to healthcare. So how I got to that is I left Pager duty after building out that team and that product line, which was called Event Intelligence, which is how you handle high frequency alarms.

And as part of my sabbatical, I joined Search and Rescue. here in Washington State, both in King and Chelan County. And the genesis of Colm Wave comes from my time in search and rescue. Because, you know, when you're going down a trail, you know, to help someone, it's sort of like you're hiking. But you're more heavily loaded. You know, you're trying to move faster. But it's usually, you know, a couple, three hours to get to the person. And so you have time to talk.

And that's where I first started hearing about clinical alarm fatigue in ICU's from doctors and nurses that were part of the go team. And I'm a big thing. Where did you, where did you hear that from? Because I'm going to ask this in the sense of if I'm someone that is a founder, a previous founder or whatever. And it's like I'm searching for my new, my next big thing. Like so did you stumble up across that or is your sibling a nurse and an ICU? Like where how did you decide this was a problem? That was it. Because like I said, I really like patterns and I really like patterns that are proven themselves. So the instant I heard. heard about this issue of alarm fatigue in ICU's. So I wonder if this is the same pattern. So I started reading a bunch of academic papers, which is where I usually go. And what I found, which was fascinating, is that the first paper about alarm fatigue and ICU's was in the 50s. And in the last like 10 years, there's a full on hockey stick motion of papers about alarm fatigue. So I was like, that is interesting. I'm going to dig into that. But then that's the big challenge, right? Like how do you go? How do you cross domains, right? I was, you know, I had a light, my career was spent in enterprise IT operations.

How do you move to healthcare where you have no content? And so, and also famously that tech has had a hard time having decent outcomes in, in healthcare, you know, so that might have been a challenge to be like, you know, people don't really have success bringing tech to health care necessarily. So So where did you get the balls to be like, I can match this pattern to that? Well, thankfully, I have very large balls. I first started looking at how do I do that, right? Because you have to get the validation of the idea. And it's hard to get the validation of idea without being able to talk to people in the space.

So that was my first challenge. So, you know, I started looking for incubators that were health tech focus. focused as a way of bridging that. And I was really fortunate in that I found the Allen Institute for Artificial Intelligence here in Seattle. It's a world renowned AI. Yeah, Paul Allen, co-founder of Microsoft, yes. And they've been around for, you know, before this was, you know, the hip thing. And their ethos is AI for the common good, which I'm a, you know, huge proponent of. And most excitedly, they had an incubator. And the last five companies out of the incubator were health tech. companies. So I was like, okay, that's where I want to go because I want to be able to use, you know, Paul Allen's connections and rep in order to be able to ask questions to hospital C-level execs. So I applied, was accepted, and we remained to this day the fastest company that

have ever gone through the incubator going from inception to customer validation to first funding in five months. And that was because I also, in addition to patterns, really like working with people that I've worked with before and have been successful with. So I brought over all the key members of my team from the previous round where we solve this problem enterprise IT to solve this problem in healthcare. And so it's much easier when you're exploring an idea to go in and say, hey, I'm a researcher from AI2. Can I ask you some questions about nurse burnout or alarm fatigue? And invariably, you know, the C-level execs of hospitals would say, sure, let's talk. And that's how I was able to very rapidly iterate and increase my understanding of the space, the problem, the impact, potential

ROI, and so on and so forth. So, you know, that AI2 angle, I feel like, was an enormous bootstrap for us in terms of being able to build calm. Well, that's almost what I was going to say, because again, you're bringing over the playbook from what you had success with in a different space. But what do you feel about the, I kind of ask this a little bit, the AI moment in terms of people now being, like, was this technology available? Was this possible a decade ago? Like, so is this a moment? No, go ahead.

No. I would say that without COVID having happened, we would not be where we are today. Interesting. And the reason being is twofold. One, the nursing situation has always been hard, right? They're like, you know, nurses have been like, we can take on anything and we'll do anything and, you know, it's all about our patients. COVID broke the back of nursing. And it broke the back of nursing because now you had like, you know, whole units of people on ventilators.

And there's nothing you can do about it, right? They expired over and over again. And, you know, for nurses, that is really hard. Not only the workload, but also just being in that environment where, you know, people are dying. All the time. The human load. Yeah. human load. And so, you know, there was enormous issues of nurse attrition during and right after

COVID, like 30% a year. And hospitals can't function without nurses for obvious reasons. The other thing that happened as a result of COVID is hospitals were more willing to share their data. Because before it was all about, you know, this is our data. It's very tight. You want to build any kind of system. It has to be on-prem. And I don't build on-prem, right? Like, I'm, I'm cloud native. I built SaaS platforms. And so that's where your ability to scale comes from. So prior to COVID, hospitals wouldn't have built, you know, VPNs to an outside entity and streaming the data to them and, you know, working with all these technologies. So because of COVID, they had to start breaking this stuff open for reporting and for everything else.

Before I come back to some of the recent stuff that you've been doing, let me ask you about the idea of again, you being an operator and a multiple founder and deciding that you want to go again. Yeah. So again, for people that are out there thinking if I'm in his shoes one day, do I know I want to go again? How much time do I want to take in between things? What would you say to someone that is out there that is like maybe I'm going to be a multiple founder person. What is the thing that gets you in, like gets the fire under your butt to do something again

after you have had success, after you've pushed a rock up a hill? Yeah. What is the thing that makes you realize, hey, I need to do this again? I'm a glutton for adversity. No, punishment is. It's, it's, you know, I think part of it. it is ADHD. I like learning new things. I personally am an autodidact. So like every startup is a new opportunity to learn a whole bunch of new things. Right. And so, you know, I've had exits in my

career, but for me, it's really not about exits. It's about how to imply tech meaningfully for tangible human benefit, right? Like, how can we do great things? And, you know, if you build a great business around that, that's awesome. But it's really about like solving things. foundational problems that affect people. That's my true north. The search of knowledge and doing things that are meaningful. Well, but on a deeper level, yes, you have another good idea. Like something is inspiring to you, but what makes you want to go again when maybe you have had success financially, you've had success professionally, and you could rest on your laurels. what is the thing as a multiple founder that says this is worth doing it?

And I say that in a way of like young people are like, this is how, this is the spark. This is the idea that's going to make my name. But what is the thing after you've made your name? Resting on your laurels is boring. Really? That's a good answer. It is. It's really boring.

Like I tried that. When I was on sabbatical, I was on. sabbatical for a year and a half. So part of what I did during that sabbatical, and that's where the impetus for Calm Wave came from was I got a break. My brain got a break, right? Because the intensity of startups is huge, right? Like, you're all in. You're thinking about it all the time. You know, your partner's complaining because you're thinking about it all the time. What I did is, right as COVID was going on when that sabbatical happened, my wife and I bought 17 acres up in the mountains. And it was a property that was, it was overgrown by probably

50, 60 years. So I spent that year and a half brushing it out with an excavator and a tractor and bringing it to where it would have been if there were periodic forest fires. And that's when I was also doing search or rescue. And I think it's having that break then kind of builds the fire back up because the intensity of startups is immense, right? Like you're going through the ringer. It's fascinating because you're learning so much. But it's also really hard and fraught with uncertainty. but that's okay, right? It depends on what you're looking for. It's definitely not stability in a startup.

I think I've said this before at various points in the show, but I did have an exit and retired a little too early in my life. You get it. It's not boredom is a thing. I respect people that do walk away and live their best lives. I have nothing against that. But also you realize that, like, what am I going to do at some point? And like if you like the game, you get back into the game.

And that's what it is. Okay, so before we close, I want to come back to that new case study that we mentioned obliquely that came out today. So tell me what this is about and why it's important. Yeah, so this is super exciting. One of our charter customers, which is the Well Star Health System, they're the largest network of hospitals in Georgia. It's a $8 billion a year in revenue hospital system. they're not only our charter customer, but they're also now a two-time investor, which is always a wonderful sign. And so, you know, we got our start there where they had been working on solving the problem of alarm fatigue for years,

had even done a case study with Phillips on their efforts, but when they saw what we were doing, they reached out and said, hey, we want to dig in with you on this. We're really excited on it. And they gave us access to the data, which is what is foundation of an all. this right they kind of built that trust together and I have to say that one of the things that was exciting for us is our enterprise IT operations background was incredibly helpful in working with these hospital systems because we know enterprise IT and there's a ton of enterprise IT work in building these integrations getting you know the internal firewalls configured so that the data flows properly

from the ICUs to the middle where all that stuff we had to you know help with that was part of the reason we're able to move so quickly and with them We deployed in their cardiac care unit in one of their largest hospitals. You know, it's like we're not the easiest path to get started on by deploying in a critical care cardiac unit. But now we are five months after that deployment, and the objective measures of success are profound. We've reduced non-actionable alarms by 50%. We've reduced the average patient exposure in a unit from 20. 25 hours to 14.4, so a 10-full hours of reduction of noise.

And then we've cut the interrupts that the nurses are receiving from 1,800 a day to more around 700. So there's really, like, big impact, and that's what this case study is. And that's why it's so exciting for us, because this is a hospital system saying, hey, everybody talks about alarm fatigue, but here's an example. example of a huge success. So that's what he's doing. That sounds incredibly meaningful. And like on, you know, I sometimes pejoratively say like, I don't want to invest in another chat app or a SaaS.

Like this is, this is meaningful in the real world in a way that like this is why I like to do what I do to be involved with people like you that are doing things like this. So, well, to that end, to close up, if people are listening to this and they want, they're excited like I am about what you're doing. Yeah. Are you hiring? Are you raising? If people are interested, calmwave.aI, how should people find out if they can get involved? Yeah. I mean, we're actively growing our team. Like, we're starting to move into the inflection point.

we're certainly looking for like, you know, passion, mission-driven builders. And really, if you think that, like, healthcare deserves better tech and clarity and less burnout, this is the spot, right? Like, Calmwave's mission is to make that happen, and it's starting to happen. And if you have connections to hospitals that want Calmer ICUs, which are better for everyone in there, love an intro. I mean, seriously, reach out directly, Ophira, calmwave.a.a.i. I believe that we're all in this together to make health care better, right?

It's like a place that is in such a our assistance. And, you know, the benefits are profound for everyone involved. So, yeah. Beautiful, Ophir, thanks for coming on telling us about Comwave.comwave.a. Again, one of my favorite investments of my entire career. So love it. Thanks, Brian. Appreciate you guys.