Latest / StartUp Health NOW Podcast / Taking a Breath of Fresh Air: Innovation in Respiratory Health

Transcript

Taking a Breath of Fresh Air: Innovation in Respiratory Health · StartUp Health NOW Podcast

• 0:01
  Welcome back to Startup Health Now, the podcast where we celebrate the entrepreneurs and the innovators who are transforming health. I'm your host, Logan Plaster.
• 0:09
  [gentle music] Today's guest is longtime health transformer, Sathya Alumalai, CEO and founder of Aidar Health.
• 0:16
  I wanted to showcase this conversation Sathya and I had at ViVE in Los Angeles to highlight innovation in respiratory health.
• 0:23
  If you're healthy, it's easy to take for granted the ability to take a deep breath in and breathe back out. But when that ability is taken away, it can lead to a host of serious problems. According to a study in JAMA,
• 0:37
  480 million people around the world have chronic obstructive pulmonary disease, or COPD, a condition that constricts the airways and makes it difficult to breathe.
• 0:47
  Sathya, our guest today, has taken a very novel approach to COPD and really the power of breath in general.
• 0:54
  His team has created the MouthLab, which is the first breath-based device that can measure more than 10 vital signs.
• 1:02
  Their approach is fascinating because it's really a platform for a myriad future biometrics that come from the breath, from your lips and saliva.
• 1:12
  Aidar Health got FDA clearance last year, and as you'll hear, they're working on some interesting use cases for their technology. Let's get into it. [upbeat music] Sathya, great to see you. Hey, Logan.
• 1:24
  Fantastic to see you again. Thanks for this opportunity. Yeah. It has been really interesting to just watch your progression. You've built this fascinating device, which you brought, called the MouthLab.
• 1:35
  Takes all these different vitals, uh, in a moment, uh, through your breath. Something I wanna start by talking about is the- [clears throat]... is the power of breath. Kind of why breath?
• 1:44
  Because there are some other folks in the market who are thinking about this idea of biometrics, and they're taking a different tack. But you have a unique angle where you breathe into this device.
• 1:53
  So maybe you could kinda show us how it works and explain why, why breath is powerful in, in taking our vitals. Absolutely. I've introduced our device in many, many cases. Like, so this is our device.
• 2:04
  It's, um, it's a breathalyzer, so you breathe into the device, like generally hold the device, place it in the mouth, and breathe into it normally for the first 30 seconds.
• 2:13
  And then, then you perform the lung function test, which is essentially taking a deep inhale and forceful exhale. Mm-hmm. A lot of people know of, of spirometer. Mm-hmm. So which is what is inbuilt into this tech.
• 2:24
  So why breath? Because breath is kind of considered as the mirror of the body. So there's a lot of biomarkers or volatile compounds, right?
• 2:32
  Mostly a lot of people go to a lab, they take a blood test, and then there's hundreds and thousands of information that you can get from a blood test, but that's more invasive. What is non-invasive?
• 2:42
  Like, we always looked into it, and, um, one of the things that we found was, like, mouth is a great place to collect key health information- Mm... because of the access to breath and saliva.
• 2:51
  And for us, we as a company wanted to actually focus on collecting hundreds and thousands of health information rapidly from an individual- Mm...
• 2:59
  versus collecting one or two parameters that are continuously monitored, that sometimes might be relevant, but in most cases irrelevant to a patient condition. Okay.
• 3:10
  So that's why we wanted to focus on breath, because we don't wanna stop with one-off solution where it measures two things and then try to find- Yeah... value with that, versus collecting a lot more information.
• 3:22
  So that's why we went into breath. And also from a data science perspective, we need more data. Okay. More unique data, and no better place than mouth that we can collect this health information. Okay.
• 3:33
  So you've got the way to breathe in. You have the spirometer. [clears throat] So obviously that's helpful for someone with, like, COPD, I'm guessing. Exactly. Um, but then you mentioned saliva. Mm.
• 3:42
  Kinda what's the power of incorporating saliva into this, uh, into this panel? Yeah. I think same, same like the breath, which I was saying, right?
• 3:51
  Saliva also contains, like, a small concentration, like, not as much as you see in, in blood, but there's a small concentration of all these compounds, like, like any, any kind of, um, markers, biomarkers that can show if a condition exists within a patient or a user.
• 4:10
  So for early diagnosis, it's essential that we find this information from the body, like non-invasively.
• 4:17
  And, and a lot of tech that are there that are focused on urine-based analysis, but saliva is shown to be, like, the second-best, like- Okay... uh, biomar- I mean, like, place where we can actually collect biomarkers.
• 4:30
  Yeah. So that's why, like, we are at a place where we collect both saliva and breath. And also another cool thing about our tech is, like, we also collect information from the lips and blood vessels in the lips.
• 4:41
  Oh, okay. Because if you could see, like, I mean, inside the, uh, lips, you don't have pigmentation like anywhere else in the body. Like, if you see, like, I mean, Apple Watches, they collect information from the, um...
• 4:52
  from on top of your skin. So there's a lot of pigmentation. So shining light and then collecting information from IR and other sensors- Mm... is far more difficult versus collecting in the mouth.
• 5:02
  Which is why, like, we established a platform more than a medical device. It's a platform- Got it... where we can add, like, hundreds if not thousands of sensors so that you can capture everything.
• 5:14
  You're not stopping with breath or saliva. You also collect from blood vessels, and also use lips as a way to collect other parameters. And my understanding is even as you're holding it, you've got- Exactly...
• 5:24
  you've got the grip of your hand, so. Exactly. Okay. So the platform idea is very interesting. I mean, in my experience with the market, what you have is incredibly unique. Um, people are, you know...
• 5:34
  And I'm guessing that's partly because it's challenging to put this all together. So tell me a little bit about that journey. I understand that you are unique because you got FDA clearance, uh, approval,
• 5:46
  uh, I think it's clearance, right? Yeah, clearance. Yeah. Clearance last year. So tell me about-About that process and kind of where that puts you today in terms of coming to market. Yeah. Absolutely.
• 5:56
  We are only, and the first and the only company today to have a device that can measure more than 10 vital health parameters.
• 6:05
  And for us, we had also the advantage of the only company that actually got a clearance in, in a single submission to the FDA. So for us, it's been an almost an eight years journey. Wow.
• 6:16
  So when we started off, um, and I've spoken about my mom in many of these interviews, so she has been the inspiration and I- we wanted to build something that's far more easier for older population to use, something that's seamlessly integrates into the life flow of the user, and that's why this device came about.
• 6:35
  And also taking into account, we, we, we spoke to a lot of users trying to understand, like, how easy it is to use. What is something that they use on a daily basis?
• 6:45
  We found, like, a toothbrush, or a electric toothbrush is a, is a great- Yeah... uh, example, where people use that every day. It's a habit. They don't tend to... Like, somebody's not telling them to do it every day.
• 6:56
  So what we thought was, like, we, we took into all these things. We had a lot of user interviews to understand what is a requirement.
• 7:02
  Then we did a lot of assessment in terms of where we can collect all this health information. Mm-hmm. Then we put both those things together and then came up with a first version in almost, like, 2000, I would say, 12.
• 7:13
  Uh [laughs] Okay. And, uh, way before any of these things existed. At that time, the tech was not available when, when you're talking about machine learning or AI. Yeah. It was still at its infancy.
• 7:23
  So people always ask us, like, "Why do you have to measure all these things? Like, how is it gonna help you?" So for us, it, it's a journey, and then we went through this, like, almost...
• 7:32
  We, we had a pre-submission with the FDA in 2015, and we actually got our clearance in 2021. Wow. So it took, like, [laughs] almost six years for us to actually go through it.
• 7:43
  But the best thing is we had constant communication with the FDA and our FDA reviewers, so they have been phenomenal and, and very helpful, which is why we stand here.
• 7:52
  Because most people think FDA is, like, really, like, some- Adversarial? Exactly. Okay. But not- But they have been-... in your experience... amazing. Okay. Like, for us, it's, it's, it's been- That's great to hear...
• 8:01
  a great opportunity. Yeah, I mean, that's something that I always talk about, because FDA is your friend, a partner- Hmm... who's helping to bring better tech to the patient community- That's ne-...
• 8:11
  and not somebody who's here to prevent your technology to be out in the market. I love that. We'll clip that out. We'll send it to the FDA. Sure. I'll be great. [laughs] You know, no, that's nice to hear.
• 8:18
  Um, let's talk about where the data goes. Yeah. Because sometimes there's this fear, maybe it's on the clinician side- Mm-hmm...
• 8:25
  that a, that a new piece of technology is gonna just send a, just a, you know, fire hose of data and, and it's gonna be more work for me to an- to analyze it.
• 8:35
  So how are you making it so that it's usable for the clinician? Absolutely. No, I think that's one thing that has been a constant question from hundreds of physicians who we have spoken to, right?
• 8:47
  They always ask this question, like, "Hey, I love all these things. You're measuring all these different parameters. I, I understand the value it can create, but it's, it's a lot more work for me.
• 8:57
  I don't want to look into all these different data stream. If I miss or misinterpret something- Hmm... then I would be under trouble," right? Interesting. So- It creates an obligation- Exactly...
• 9:05
  to understand everything you've been given. Exactly.
• 9:08
  And that's why, like, e- even though physicians are optimistic and proactive in terms of helping patients and having a lot of data, they don't want to go to the trouble of
• 9:19
  r- I think, understanding all this information and then, like, decoding those things, which is why we were working on this for almost six years in terms of let's take all this data, let's simplify it, let's give one number or, like, an index that can actually tell you exactly where the patients are.
• 9:36
  So after six years of work, and, uh, last year we spent almost a, a year-long effort in terms of, like, managing and, uh, collecting data from over 200 patients who are veterans, like, or part of the VA health system.
• 9:49
  So we collected all this information, and we were able to retroactively validate our algorithm. So today, we built something called IDI, or A-I-D-I, AIDR Decompensation Index.
• 10:02
  It's simple index that's from, like, ranges from zero to one, where zero is considered as the baseline, the value of the patient.
• 10:09
  Where, and one is, like, considered as, like, high risk or somebody who's about to be hospitalized. Okay. So that is the only thing that physicians see. Like, from when they log into our- Interesting. Okay...
• 10:20
  AIDR Connect platform, physicians, first thing that they see is, like, in terms of what the patients are, what's their health trends are.
• 10:27
  But more importantly, they have one index number, which is ranges from zero to one. Simple. Right?
• 10:32
  So for them, they can easily understand, like, okay, where this patient is, and we give clear indications about, like, why this index number is this, what are the parameters that are causing this- Hmm...
• 10:44
  and what are the weights that are given to these patients. So physician have the full liberty to go change those weights, like, change those baseline that we are creating. Okay.
• 10:53
  But for us, since we have actually validated it through a clinical trial, it's far more easier to showcase that.
• 11:00
  So, and, and we are very happy and excited to say that we have actually submitted to the FDA this algorithm- Nice... and we are expecting a clearance.
• 11:07
  Once we get the clearance, this algorithm and the index would be available for every single physician or care team member- Wow... who's actually looking into the data. Interesting.
• 11:17
  Let's talk about going to market, because what you have is FDA-cleared, CE mark, right? You, you've done all that work.
• 11:24
  You've created something new, super helpful, and yet, and now you're at that phase that, that any, uh, startup hits where you have to get this into people's hands at scale.
• 11:36
  You have to really start doing that work, and that is incredibly difficult, um, just to, to, just to change behaviors. Obviously, this has sort of niche or, or specialized, I should say, use cases, clinical trials- Sure.
• 11:49
  Yeah... COPD, but it also has consumer- Yeah... use cases. So where, what are you thinking about how to- Yeah... get this into people's hands? What are your goals?
• 11:58
  Uh, what do you hope to see over the next couple years?Do you want it to be more consumer, or do you want it to be more specialized? Yeah.
• 12:05
  I think the ultimate goal is to have this device in at least a billion people's- Okay... hands, right? So that's our goal, and we wanted to impact a billion people in a positive way.
• 12:14
  So right, right now, the challenge we have is, okay, scaling our manufacturing, right? So how many devices can we build? As, as a hardware, like, component- Yeah...
• 12:23
  a company with the hardware components, far more challenging, because if you have to generate a dollar in revenue, you have to put in, like, $1,000 ahead of, like, for manufacturing, setting it up, deploying it- Yeah...
• 12:34
  and making sure everything happens. So which is why, uh, we're looking into where is the best place where we can touch more people with limited number of devices. Okay.
• 12:43
  We found, like, nursing homes or senior living facilities- Makes sense. Yeah... a fantastic opportunity.
• 12:48
  Plus also, there are people who are there to support them, and then provide them the device instead of expecting the patients themself to use it. Right.
• 12:55
  So which is why we're getting a lot of inbound requests from a lot of nursing homes right now- Okay...
• 13:00
  skilled nursing facilities, because they all wanted to im- apply this RPM or remote physiological monitoring programs, but they don't have the resources to do that. Mm.
• 13:10
  And today, the, the sad reality is, like, all the nurses today have to take at least three, I mean, vitals from patients every day, at least thrice, three times a day.
• 13:21
  But because the technology requires, like, charging the devices or it requires additional care, cleaning and all those things, people don't actually take care of that, even if they have, like, a really expensive- Mm...
• 13:34
  like Bell-Shalen or other devices. They end up going to Amazon and buying the cheapest blood pressure cuffs- Really?... and pulse oxes, which is not FDA cleared, and- Wow...
• 13:43
  they have no idea it is, because their objective is like, "Hey, I want to take measurements," because unfortunately, devices get missing. So I, mean, we don't blame the nurses, because they are doing the hard work.
• 13:54
  They are trying to do, take care of patients, because they are kind of the real caretakers for patients- Yeah... especially in a nursing home, which is why they're doing their best.
• 14:03
  So what we are trying to do is, like, support nurses- Mm...
• 14:05
  with the best tech that we can provide, because today, nurses use multiple devices, and they have to take down on a piece of paper, and then again, go to a system, and then update those information, validate those information.
• 14:18
  Instead, our device, they just go in with a, with a hand unit. E- each patient gets their m- own mouthpiece, so they put in the new mouthpiece, have them take a measurement. The data goes to our cloud.
• 14:29
  From there, it goes to the electronic medical record. Right now, we are connected through PointClickCare- Okay... which is the largest electronic medical record provider for nursing homes. Nice.
• 14:39
  So as soon as they take the measurement, it goes directly there, so n- nurses don't have to even take- Wow... uh, any, any data. No notes. No notes. But we also give a tablet where if they want, they can put in notes.
• 14:51
  They can put in other factors- Wow, okay... because we don't measure, um, parameters like weight.
• 14:56
  So they can actually use our device, uh, our platform to actually upload information about their weights or any medication information. Everything is there. Okay.
• 15:05
  And our platform also reflects whatever is there in PointClickCare. So it's a one system- Mm... that they can actually rely upon and use it. They don't need any other system to use. Got it. Okay.
• 15:16
  Okay, so it sounds like the inflection point here that you're at is the, is the factory side, making sure that you, uh, are able to produce these at the, at the cost you need to and the right place, and then kind of breaking into, uh, skilled nursing facilities at scale.
• 15:31
  Yeah. And really, uh, but then- Absolutely... but then you mentioned the whole billion number, like, that's sort of like, that's just the beachhead, right? Mm. And then there's the sort of consumer...
• 15:39
  Uh, there's a lot of different use cases for this down the road. Yeah. But, but obviously, you have to, uh, prove it out in different arenas. Yeah, absolutely. Okay.
• 15:47
  And then also from an algorithm development perspective, right? Mm. So right now, we have the first submission on a software as a medical device.
• 15:54
  Once we get the clearance, then we need to start focusing on condition-specific algorithms. I see. I see.
• 16:00
  So it's gonna be, we ha- already have, like, a lot of data from patients with COPD, heart failure, chronic kidney disease, asthma, and hypertension.
• 16:07
  Now we need to start, like, pulling all those data and showing, like, how the algorithm can be modified for each condition so that, like, that can be, like, separate submissions.
• 16:17
  So our goal is to not only build tech, like sensors and add new, new sensors- Yeah...
• 16:23
  but also build algorithms so that it actually augment whatever data that we're collecting and then make it as simple as possible for not just the physician but also to the end users.
• 16:34
  I- is it fair to say that since you're doing both sides of that equation, the sensors and the algorithms, that that's creating a bit of a moat around what you're doing because of the, the complexity of s- uh, disease-specific algorithms that you're getting approved?
• 16:46
  Yeah, absolutely. I think this is the... Hardware is our biggest moat, and I think if you've seen a lot of hundreds of companies there, they're relying upon other hardware companies. Mm.
• 16:55
  They're relying upon their own competitors- Mm... to actually survive in their business. Interesting.
• 16:59
  But we are the only company, small, but we're [laughs] the only company in the world who has, like, hardware, software, and also the entire technology surrounded this tech- like, this device, so that we're not relying upon any other external sources, and it's all, like, integrated into one system, going all the way into EMR as well.
• 17:19
  So if you're using our solution, you don't need to find another avenue, another sensor or anything, but rather just use that to actually give a comprehensive picture of your patients or your members every single day.
• 17:33
  Got it. Well, I wanna, I wanna see this get into more people's hands. Uh, i- if a person who runs a giant chain of skilled nursing facilities is watching this, you know, what should they do?
• 17:44
  I think they should reach out to me. Yeah. Like, I'm, I'm, I'm running this effort in terms of going to all these different nursing homes today. Yeah. Uh, essentially, like, there's a new revenue opportunity. Okay.
• 17:54
  A lot of nursing homes don't even have- You can get reimbursed for some of this. Exactly. Okay. They've, like, RPM programs. You can get reimbursed. There's...
• 18:00
  We also can identify within their, like, nursing facility how many patients can qualify for these programs- Okay... and how they can get reimbursed. There's new ways of actually- Okay...
• 18:10
  getting, uh, a single code so that they can, like, m- one physician can manage maybe thousands of patients. Wow. Or n- one NPI number can actually be used, get reimbursed.
• 18:22
  So a lot of nursing homes are, like, the bigger ones are trying to take advantage of that. Yeah. But still, it comes down to technology support, reporting support, compliance. Yeah. We have done that.
• 18:34
  We have- Interesting. Okay... we, we have the technology, the device, as well as the compliance support. Like, essentially, we can send them reports. We can help them to submit to CMS for reimbursement. That's great.
• 18:45
  So it's a one-stop shop- Yeah, that's great... where we can do everything for the nursing home providers. All they really need to do is, like, trust us and then onboard us so that we can- Yeah...
• 18:54
  actually provide them with this full suite, plus additional revenue opportunity. Nice. Awesome. Well, it's been so exciting to watch your journey. Thanks for stopping by and letting me know what's going on.
• 19:04
  You know, seeing this use case, just a reminder of how many different groups can benefit from what you're doing, but also what a journey you're on, each disease-specific algorithm, getting approval.
• 19:16
  You're doing the work year by year, and it's, um, and it's, it's all coming together. So excited to see what you do next. Thank you so much, Logan.
• 19:24
  It was, again, a pleasure talking to you again, and look forward to another conversation. Appreciate it. [upbeat music] Thanks for listening to Startup Health NOW. We'll be back again with another episode next week.
• 19:36
  [upbeat music]