IoT Leaders
IoT Leaders

Episode 29 · 2 weeks ago

Reinventing Healthcare with IoT, AI and ML


What if there was a way to change the trajectory of human health? Our era is marked by the proliferation of inexpensive, but high quality IoT sensors that enable advanced early disease detection. A way to measure human physiology in a continuous manner.

That’s exactly what data sciences company Biofourmis is doing. CTO, Milan Shah explains how the combination of IoT, artificial intelligence, and machine learning is enabling clinicians to interpret patient’s biometric data and identify earlier warnings than ever before. This allows clinicians to intervene earlier, improve patients' quality of life, and provide them with better outcomes.

Join us as we discuss:

· How Biofourmis is identifying physiological deterioration using ML and AI algorithms

· The types of medical-grade IoT sensors in the market today

· The power and potential of biomarkers

· How the COVID-19 outbreak was managed in Singapore with Biofourmis’ solution

· Why personalised predictive healthcare is the future

Show Links

· Check out Biofourmis

· Follow Milah Shah on LinkedIn

· Connect with Eseye on LinkedIn or Twitter

You're listening to iote Leaders, a podcast from s I that shares real IoT stories from the field about digital transformation, swings and mrs, lessons learned, and innovation strategies that work. In each episode, you'll hear our conversations with top digitization leaders on how IoT is changing the world for the better. Let IOTE Leaders be your guide to IoT digital transformation and innovation. Let's get into the show. Hello and welcome to the IoT Leaders podcast with me your host Nicole c O s I. This one, I actually think is my favorite, one of all the ones that we've done. I think this is my favorite. And the reason is it is such well, it's a great story. But this is a company Biopomis, as you'll hear, that is trackling an enormous problem which essentially can be summarized as reinventing healthcare. And although that sounds hugely ambitious when you think surely no one can take that on, are they being funded almost half a billion dollars by some of the top vcs in the world who believe in what they're doing. They're already active in the market and they are essentially identifying disease using machine learning and AI algorithms to identify disease before the patient sees it and therefore get early intervention. And you'll hear a great story about their journey from Melanchar the CTO and where they're up to and what else it could be used for going forward. So this is a real Silicon Valley type play. They're based in Boston, Massachusetts, although they started in Singapore, and so it's all here in the podcast. I think you're really going to enjoy it. And so with that, I will then handover to my discussion with Melanchall the CTEO of Bioformis here we go. So Milan, welcome to the IoT Leaders podcast. Great to have you. Thank you for having me. Nick Well, really looking forward to this one that we've been working together for a few years. And I like to say that Bioformis is not a company that's trying to solve a small problem. I mean, you are massively ambitious as a company and quite a bit down the line you're CTO of Bioformists. Perhaps we can start, maybe just a little bit about yourself, Milan. I always like to sort off the podcast with just let people understand who my guest is. So maybe just a brief part in the history of of yourself and how you became CTO of bioformis sure. Thank you again for giving me the opportunity to be here. It's been a pretty amazing, very very I consider myself very blessed to be able to do what I'm doing. So I'll start with my actute work, which I did at m I T. This was about twenty seven years ago, and at the time I worked in the c sale, the Computer Science and and AI Laboratory, and AI and machine learning were you know, in a somewhat rudimentary stage. There were great computers, massively parallel computers being built to do AI m L, and you know, really the big thing that everybody was trying to figure out is what would we use a m L for? Right, What could be the applications of this kind of technology. So that was twenty seven years ago, and since that time my entire career has been really focused on both those elements, large scale computing and and a I m L. Now, my earlier part was more about large scale computing. Distributed computing was sort of the implementation, not necessarily the supercomputers part, right, but the idea of distributed computing really was starting to take off, and then sort of the Internet happened, and and now we have cloud computing. So distributed massive scale computing is is something that's here now very easily available. My journey started. Career...

...journey started at Microsoft, where I worked on the initial versions of Microsoft Windows and t again on the distributed file system. I ended up building Exchange, which is their email products, still very much in use today. And then I moved on to a lot of cybersecurity type of applications, a single sign on and so on. The recurring theme over there was an underlying team in all of these uh cases was how can we also use some amount of machine learning to to manage the scale too, if you will. Right, certainly, the last thing I was doing in cybersecurity was essentially monitoring, collecting metrics from computing. So you are, let's say, a large infrastructure company like a PayPal or an eebay and people like that, Right, you're running hundreds of thousands or Facebook, You're running hundreds of thousands of computers, and you're trying to figure out which of them might be under attack, which one of them might already be hacked, and so on. So the general idea was, if we collect enough metrics from all of them and we understand what their baseline behavior is, maybe we can use AI and mL to identify which ones of them are skewing off the baseline and potentially are have been hacked and so on. Right, it's a tough business, tough technical problem, mostly because computers don't really have a consistent baseline. Right. And while I was working on this kind of a field, I managed to run into cool Deep Rachpot. He's the CEO and founder of Bioformists, and he had recently decided to move his headquarters to Bioformist. This was on the heels of the Series A or CESB, I should say, from Sequoia the forty five million, So he was ready to you know, really expand the technical team and then take the company on it's onto its full potential. He was moving his headquarters to Boston, and that's how I got introduced to him, and when I learned about what he's trying to do, essentially collecting metrics from human beings and trying to figure out when somebody deviates off of a baseline and therefore is likely deteriorating from a physiological perspective. If we could detect that and of clinicians and earlier warning than there's ever been possible, then they could intervene earlier, produce better outcomes, the patient benefits from a significantly better quality of life. Overall cost to the systems go down, and we fundamentally, you know, can change the trajectory of human health. Of uh, you can actually anticipate a day and when when given systems like ours and automatic infusion systems, you could actually have self medicating human beings right. Wherefore, many many cases you could detect the onset of some illness and automatically administered medicine, keeping the human being essentially disease free for a lot of what is common diseases today. Yeah. So, once I understood what he's trying to do, the fact that humans physiology does actually, as opposed to computers, do actually have a very consistent baseline in many levels, I sort of understood the power of being able to apply A I. M. L techniques to the problem of early detection of disease. And you know, one thing led to another, and I decided to join our forms and here we are, here we are, Well, there's a lot there and It gives the listeners a good idea of why so many people think that this is such huge potential, you know, and to unpack that. I guess when I first heard of bio formats. So I've talked to other people about bio formats. They say, oh, yeah, medical company. But you don't describe yourself as a medical company. In fact, you've talked about AI and m L and when we spoke previously, you say, no, no, no, we think of ourselves as data sciences and you talk about bio markers. Maybe you could just go a little bit more into that, because it helps people differentiate between you know, is this just a sort of a big version are you competing with the Apple Watch? Or is this just a big version of remote patient monitoring? And it's not. But to understand why it's not, you kind of have to go down the A I, M L bio market route, don't you. Absolutely? Absolutely so, We certainly consider ourselves a data sciences company. And what is really unleast the full potential here not only about formats, but I'm beginning to see... across the industry, right is one way to really think about it is if you look at clinical practice, medical practice starting from years ago when Hippocrates basically realize that, you know, illness and human maladies are probably not caused by some you know, superpowers and the gods and and so on and so forth, but rather they are caused by something else where it is possible to observe, draw conclusions, and come up with treatment plans. Right. Just the very idea that that you know, human malaise was not being caused by the gods, but by caused by something else that could actually be treatable. That is what set off the entire era of modern medicine years ago. Right. But therein lies the catch. The way it is is you first observe, then you draw conclusions, and then you come up with the treatment plan. Right. So translated, what happens is all clinical practice effectively starts when the patient visits the doctor, and then you know, the doctor does their measurements and and draws inferences and then comes up with the treatment planet if you go right. What technology today has really enabled is something which flips the whole model on its head. Now, because of the proliferation of very inexpensive and very high grade sensors, everything from optical sensors to ECG sensors to even fancier things like a galvanic skin response sensors, cameras, what have you. Right, all of these sensors have suddenly become a very inexpensive and b extremely high quality. It is now possible to measure human physiology on a continuous manner. And what has happened here at bioforms is that as we look at all these common physiological phenomena or your pulse rate, your ECG wave form on a continuous way, you can start doing math on these continuous wave forms. You can take the first derivative, you can take the second derivative, You can do correlations with each other, and so on and so forth right, and as you start doing that, you come up with what we call bio markers. So these are essentially mathematical constructs that seem to be correlated, mathematically correlated with certain disease conditions, certain disease progressions, and so on. In many cases they don't even have a name. For obvious reasons. It has not been possible to to do this kind of measurement and to do this kind of calculation before, but now it is. It's it's you know, from a math perspective is relatively straightforward stuff. Right. As an example, if you took a pulse wave waveform and you took the first derivative, well, that's just measuring the acceleration of the pulse wave. In some ways, it's a proxy for how the heart is beating, right, much pressure it's generating. So even though the first derivative of pulse wave is not a mathematic is a clinically understood quantity, you can see that intuition would lead you to believe that the first derivative of a continuous pulse wave form is probably correlated to blood pressure, which is correlated to lots of disease conditions. Right, So you can take these kinds of biomarkers and go off with that, right. Or alternatively, you can think about let's say voice. We all understand that when you start falling sick, oftentimes your voice changes. So once again, you can take voice samples of people of patients and do signal processing on them and understand the deviations of their signal processed voice from a baseline when they were healthier to now when they may not be, and you can again collate that to disease progression. So the act of sensing and the act of this massive computing power available has allowed us to define a whole bunch of biomarkers which seem to be correlated with disease progressions and disease conditions, which can then be used to provide a signal for earlier intervention and so on, so forth. So it's been a it's a fascinating, fascinating new world out there. It totally is. And we've had a few podcasts guests in the classical healthcare field who are also aside customers and will get onto that. But there is a common theme in the a lot of people saying, is the old model you described it, the serial supply chain. You know, something takes whole after two or three days,...

I realize I'm sick. After another two or three days, maybe I'll ask for a doctor's appointment. After another I don't know what it's like in Massachusetts, but in the London area, after another five days, maybe I'll get an appointment. I mean, by the time I get there, I'm pretty sick. And then and it's a reactive model. But what you're describing is a proactive model, but with the potential to be a preemptive there's something going on. You don't know it yet, but the AI thinks there's a there's a as an extra sent correlation between six factors. Therefore, intervention behavior change isn't it? And I guess you could almost it's massively long tailed personalization as well, because maybe the bio markers for one person means something different to the biomarkers for another person, and there's just no way you can do that with a linear supply chain reactive reactive system. It has to be personalized, it has to be computes and a I m L and it becomes proactive and preemptive, and it's the holy grail if you like I of healthcare. Absolutely, I think you nailed a lot of important topics that all tease out a couple. One is the whole idea of personalization. So you're absolutely right. Right. So, now, once people have realized the power of a m L and how it can be applied to healthcare predictions and so on, the next hurdle that people have run into, so to speak, is the idea of well, I m L. How does it traditionally work? Right? You collect a whole bunch of sample data from essentially a cohort group. You train some kind of a model and then you supplied with data from a new person that it has never seen before, and then it will have predictive powers based on that initial training, right data. Unfortunately, as you correctly identified, every human being is effectively different from every other one. You have your own medical history. You're slated for a knee replacement surgery three months from now, and you just got COVID while you were a heart failure patient. That strits it all right, It's very difficult to try and find a suitable cohort group that we could train a model on which will work for you. Right, So Biofarms has taken a very different approach. We don't train our models on cohort group data. We train models based on your own data. So, by now understanding human physiology as well as this basic change in strategy, we can take the baseline our algorithms in usually in three to six hours, we automatically what we use do use the cohort group data for is to project if your initial six hour data looks like this, then what will be the effect of nocturnal cycles, what will be the effect of circadian rhythm etcetera. We can project without actually having to measure, and so we can start doing predictive analysis between three and six hours from when you will on board onto our system, and after that it's completely personalized for you. Then the predictions are very very accurate from that perspective. Incidentally, a lot of this is FDA cleared. So it's I love that about the space we are in, right, that you can there's a a framework in which to measure these claims. Right, So some of our algorithms are cleared by the FDA, which means that the exact how do you interpret this data and what kind of clinical action you can take based on this data is documented in a very public form and validated by a obviously extremely accomplished body like the FDA. Yes, which is hugely important because of the implications of the initial interpretation that you're making of the data. More he goes to the clinician. Yeah, we get back to approvals in the market in a little bit later on. But I was reading some of the press releases when you did these big funding grounds. Well, actually what journalists wrote, and one of the phrases that jumped out at me is this fact that you once you've created this knowledge through the AI m L layer. There's actually two very different audiences for it. I mean, there's it talked about the sort of healthcare providers and the clinicians who obviously want to get this data because of the proactive preemptive bit. And then I'd also talked about pharmaceutical companies, which and the sort of drug approval process which I've never...

...thought of. Maybe we can deal with each of those in a one after the other, because the second one takes it to a whole new layer level to do with how drugs are introduced in the future, which is really interesting. But let's deal with the first one. So let's just say I'm a clinician or I'm a healthcare provider, I would be a potential customer. Then bio form is because I want to get this data, right, correct, Yeah, you want to get the advanced warning system is the more important part. It's careful because teinicians are already inundated with so much information. The last thing they want is essentially data from a continuous monitoring system. That is, forget about a clinician, but humans cannot digest continuous stream of of data anyway, right, So it's not so much the data that they're looking for, but the AI m L interpretation of the data and time the signal to take a look at the patient for a potential intervention, and then the clinically established sensitivities as the call it or accuracies of this signal to know that, hey, when this signal is generated, there's a nine chance that there is going to be an intervention necessary, so it is worth my time to now take a closer look at the patient and figure out what's going wrong with this uh patient. So lots and lots of applications across Again, as I mentioned, our technology is personalized. It is also disease agnostics, so we are after our m L will tell you about physiological deterioration. Right. Is interesting to a clinician because then they can apply their skills to figure out what might have gone wrong. The technical trade off, of course, is that while we are disease agnostic, well we are disease egnostic, which means I contactually when I tell you something might be going wrong, I actually my system has actually no idea what it could be. Right. Where this is applied is in a couple of very interesting cases. So if you take a let's say chronic disease, right, and the patient journey. So let's say heart failure is a good example. Right, So just turned fifty or is it. You do your annual check up and just the fact that you turn fifty, you have no symptoms, there's nothing wrong with you. But you might have a family history of heart disease. So the doctor will tell you, well, just because of that, you are an increased risk of heart failure disease. Right. The typical prescription at that time or advice would be to change lifestyle, change dietary just because you happen to have a family history and you are at higher risk. Right. And then you fast forward, and what happens. You might start experiencing some symptoms, right, shortness of breath, something along those lines, right, and they might put you on stating. So maybe your cholesterol is too high and can't be controlled and you could benefit from that, right, So you could turn around and they might prescribe some statins for you. Right. At some point that might escalate, unfortunately, and you might end up in an adverse event of some kind, or myocardial infarction, or you are completely out of breath and you're rush to the E R type of thing and so on, and now you come along with the regiment of medication and so on, right, and then you are the doctor takes care of it, and then you are monitored closely for X amount of time, and then you're back into your stable period management and so on. And you could go from any one of these stages to anyone back. Right, you could be perfectly stable and suddenly something happens unfortunately and there's an m I event, et cetera. The value of our system is that along all of these cases you will get typically get an earlier warning so that the doctor can especially when you think about some of the acute phases, or let's say you just were discharged from the hospital with after an acute event, you are very likely to I think there's a statistic out there that says of people who are discharged after an AE of some kind in the heart failure pace are readmitted back to the hospital within the first thirty days. And the high level reason for that, nick right, is that typically something like heart failure has to be treated with the cocktail of medicines. We're also of familiar with that, right. They essentially treat one thing but that as a side effect, so they have to negate the side effect without other medicine, and so there's a cocktail of medicines. Unfortunately, that cocktail of medicines has to be tuned. The dose have to be tuned for your particular case, and unfortunately, before some...

...of the tuning can happen, you might suffer a second incident. And it's actually the second incident that often is the more deadlier one has to be treated with much more care because you're already at recovering from the first one significantly costlier, and so on and so forth. So with the technology like ours, what happens is as you're going through that tuning system, we will give an earlier indication to the clinician saying you need to readjust the dosages because this person is actually deteriorating, and that allows the person the clinician to adjust the dosages before that second incident actually happened. So, if you have been diagnosed and you are on a dosage medication or dosage plan, our system will often give an earlier warning to the clinician to adjust the dosages, which then dramatically improves outcomes and patient quality and so on. And so forth. So it's, uh, that's the typical application rights. It's just earlier warning, earlier intervention, therefore smaller intervention, all enabled by continuous smallest ring as opposed to the as I called it, the supply chain as it works. And so that in itself is a huge benefit. But then when you told me previously about the pharmaceutical companies and how they I believe they even came to you and said, well, actually there's something else we could do with this, then I was thinking, wow, I that it never occurred to me. But then I thought, oh my god, you could potentially reinvent how clinical trials have done here. So maybe you could share that story with me. Absolutely so, Actually it came as a little bit of a surprise to us as well when farmer, So, what really happened is we're working on this technology. Our focus was cardiac space, with the secondary focused on oncology because that's where a lot of symptoms show up. An earlier intervention can have disproportionately large benefits. Right, So that was our focus, and we're doing our thing and so on, and guess what happens. COVID happens, as it turns out as I mentioned our technologies disease agnostic. Long story over there, But our technology got applied to COVID management as well, actually worked very successfully and really put us on the radar of a lot of practitioners and so on. Right, so we're on this journey and out of the blue, the big farmer started coming to us, and we're like, no, no, I'm not really sure why you're You know, you're gladly take your meetings, but I don't think we have are technology really makes any sense for big pharma. It is really about healthcare providers and so on and so forth right. And they came back and they said, no, no, quite the contrary. What's really happening is a lot of drugs, especially the class two, Class three and severe disease cases and so and so forth right, they often come with a library of side effects. You know, if you look at chemo, the fundamental idea of chemo is it's a poison. Right, Obviously it's going to have lots of side effects. So the question then becomes, how do you make these drugs safe? Well, today they approach it essentially as an open loop system from a technical perspective, right, they try to adjust the chemistry so that the ratio of benefit to risk and so on is appropriate enough for an approval. And then there's a huge clinical practice that is trying to coquote monitor you and so on and so forth. So they're big insight was, hey, what if we use a system like yours to monitor for these side effects? And not only that, but we could even go one step further. And in some cases pain medication is an excellent example. If you could give us an objective measure of pain, then your algorithms in your system could actually dictate the appropriate dosages for our medication. So they now have identified a class of a whole bunch of molecules which are in various degrees of clinical trials. Right now, we're really the only path to an empty acclearance is if it could be accompanied by a system like ours that actually makes the molecules safe to use. Because the molecule will do this thing, we will detect the side effects, or we will have earlier detection of something something is going off of the safety margins, and that will be a que to the clinician to go and adjust the dosages or maybe eliminate that particular drug until the side effects of sides and so on and so forth right, And in many cases...

...that's really the only regulatory path that is possible. Outside of that, the drug would not be safe to use and it's unlikely to meet regulatory clearances. And if you now extended to even drugs that are already in the market interest and people like that, and so on and so forth right, you can imagine that you can see one day where it will be almost unnatural to think of a drug that is taken with no control around it. Right, So in the future, we do anticipate a situation where every drug that's prescribed, even if it's fairly innocent, it's just cheap enough to have our system monitor you for any expected side effects so that we can prevent any adverse events due to the drug, and maybe we won't then have the day where I get my medication. I opened the box and I find that very tightly folded bit of paper looks like it's been folded by an our GAMI expert. And I opened it up. There's eight pages of possible side effects and w was, I guess because of exactly what you're saying, they give it control group, they can't intervene. There's no continuous monitoring. So if anything develops anything, they have to record it, obviously, but they can't sort of head it off at the past. They can't say, oh, let's adjust and whatever because they don't have this system. Therefore, the net result down the line is you end up with pages and pages of possible you know, one patient in a hundred, one patient in a thousand and all. Listen, you read this defain thing. Oh my god, do I want to be taking this? And I guess it's all because of the way the tests work. I mean, you're describing a vision where the whole FDA approval of new drugs could actually involve or require almost continuous monitoring to improve the absolutely yeah, not almost absolutely. So what would be regulated, what would be cleared from regulatory perspective, is that what we've got a combination therapy where you take this medicine and you do this monitoring, and you adjust the medicine based on what the monitor is telling you to do, and that ambuination is what will be approved or has a chance of regulatory approval. Any just the one or the other is unlikely to meet the regulatory safety standards. It's such a huge subject. I know we could go on for hours, but there are two other areas I wanted to go into it we can. The first one is your CTO. We're an IoT company. We haven't talked anything about how you do this. We've said a I, M L. But actually there is a device involved and that's where our relationship has been. And I know you initially started off with Bluetooth and then and GEN one of your devices, and then you said no, no, no, we really need cellular and ubiquitous, constant connectivity everywhere, which is where we came in. Maybe you could talk a little bit about the absolutely so, especially driven by COVID, there was the realization plus all these use cases that we talk about, right, whether it's a pharmer side and drugs, new drugs coming out the market, or just the number of diseases that we could manage and help improve the management off You can imagine that our initial solution, which included obviously the data science, but data science needs input in order to drive it. That input, as you mentioned, comes from a wearable. So ours happens to be an upper arm based wearable as a plethora of sensors in it which produces the signals which are then fed and digested by the AI, m L and so on and so forth. Right, our initial version, the current version that's in the market UH is a CE approved device and connects to over bluetooth to a smartphone, and the smartphone is the one that actually transmits the data to the cloud. Right, the system works. You know, people manage COVID with it, for example in many countries actually at a nation state level and so on. But what that has really done is just driven the realization of the value of this into the future, the potential that something as a solution like this can bring to bear. Yeah, and a few countries, so because of COVID, a few countries actually chose our solution as the way to manage COVID in their country. So, if you've got COVID in let's say Australia, the...

...standard protocol will actually send you home with our solution and then our AI will indicate when you need to be brought back in perhaps for ventilation, but with the vast majority of course recovered without any sensory admissions and so on so forth. Right, But people like that, countries like that have come to us and say, hey, outside of COVID, we can actually use this for many other disease areas. The problem, of course, is when you're trying to use it at a population level scale, trying to create an ecosystem where there is a controlled device, which is a smartphone, which obviously has to meet configurations and uptime numbers and so on and so forth, that just is not technically viable today. Right, nobody's going to carry two smartphones to begin with, and even after that, trying to keep the device in close proximity to the Bluetooth smartphone has actually turned out to be extremely challenging. People walking into the bathroom, people walk into the basement, they've got the kitchen, they leave the you know, the phone charging in in the study, and they've got to sleep in the bedroom. And now Bluetooth connectivity has been lost, right, So Bluetooth is actually just not a viable way to do this continuous monitoring. And that's what led us to saying, hey, we should just put a fog chip into the device, have it connect directly over the FOG network into the cloud. And now the patients just started to slip in our device and they're in continuous monitoring and magic starts happening, right, so that was the promise at this point, that is the reality as well, and that is really what introduced us to east Side. So we looked for a way to make this wearable work across the world in all of the geographies where we already have business, and then more and ultimately identified SI as the partner. And even though we didn't sort of realize it at the time, what's turned out is just the whole getting an IoT device to actually work in an approved manner past all the regulatory hurdles in across the world is actually a very very tall task um and certainly obviously it's you know, engineering and science. It's doable, but you can imagine for a company like us, where we have so much potentially just in our domain, you know, for me to try and allocate engineering resources to really become an expert in the FORG network part is probably not the most direct investment that we would have liked to make, right so a partner like s I has had a disproportionate amount of effect. And even though we stepped into it more on in a reputation and recommendation and so on, what we actually experienced was s i S tremendous experience and expertise, and not just in what they do, but in extending their expertise into what we do. So today engineers and support people at s I actually have an inordinate amount of expertise on my device and what it's trying to do and the implications of how it does things and so on. And that was necessary. That overlap of expertise was what was necessary to bring what we now have to the market, and that has been a big success story from our side. So today our next generation device is available and works across the world. I believe there are three countries which I didn't even know they were actually officially recognized as countries. But any case, except for those three countries, the device's people because we say we have global connectivity and we solve this problem, which by the way, everybody thinks isn't a problem because if we said a many podcasts, I just put a SEM in my phone. It works, doesn't it? Well, IoT, No it doesn't. IoT. Devices don't work like that. But that's every every customer is an education process. But yeah, there are three countries, and it's more to do with the political regime what those guys do with technology, which there are three. But other than that, it's every country in the world. And yeah, I know, it's been a great journey and we've learned a lot together and trying to do this in a relatively small device. And as you say, you know, it's you know, not only can need the patients perhaps leave the phone downstairs and go to sleep upstairs. But some of these patients are you know, in the seventies or their eighties and telling them to sink sink with the smartphone. I mean, it's just not going to happen. So sellular around the world the one button press that we've talked about getting this device,...

...because the device is the It all starts with the device and then the data goes to the getting the data into the cloud another technical problem. People think it's easy, it's not. From a variety of different operators and then getting into your AI engine. And so it has been a great journey. Let's try and bring it all together if we can. Milan such a great story. But let's try and bring it all together with one example of one country. And I know that I think you've mentioned Singapore earlier. I know that you guys are pretty active in Singapore. I think there's was the seven million people there or whatever beautiful place. Maybe you could just describe a little bit about what you've been doing in Singapore, because I believe you talked about regulatory approval and I believe you're pretty far down the road there. Yeah, yeah, yeah, thank you. So Singapore is somewhat specially in our history because we were founded in Singapore. Are two co founders happened to be student at the nationally newery the University of Singapore pursuing the h d S when you know, they decided to do bioforms and the country of Singapore has been a tremendous aid all along. They're actually an investor at this point E d. B. I is a is an investor, early investor, I should say, right, So they were very much important reason why we were able to get to this stage of our journey. But interestingly enough, what really happened is again COVID happened now Singapore, Hong Kong, all these regions. You know, they had a previous experience with stars, so they had a tremendous experience in how to manage coronavirus like COVID prior to that. Yeah, so they didn't necessarily weren't necessarily looking for technology like ours, right, And in fact, you know, in the grand scheme of things, they were very successful at very aggressively managing the spread of COVID, right. And then of course what happened is eventually an outbreak did happen. They have in Singapore there are these higher concentration living arrangements, they call them foreign workers arms, and COVID broke out over there, and suddenly they had essentially a night or in very short duration, thousands and thousands of COVID patients right, all in a very concentrated physical location type of thingia. And what they decided to do was basically take every resident of these dorms, give them one of our solutions, and then they literally set up a clinical center right in the basement or one of the floors of the dorm where they brought in some clinical staff over there who were monitoring all of the patients, all of the subjects, and then you know, calling the ones where we signal would need attention, calling them into the UH into the clinical center that was set up in the dorm itself. Ya. So that's how they managed COVID outbreak when it did happen in these highly concentrated areas, and that is really what led them to think about wait a minute, right, this we see how this works. You know, people are going about their lives, they're wearing this device, they're being monitored when it signals. That's when they brought into the hospital and they started exploring with us the idea that outside of COVID, can we used this for many other disease areas as well? And the answer, of course is yes. I mean, it really happens exactly like you describe, right if you yourself false sake, you know, you will wait for three days until it hits a certain threshold, and then you'll seek an appointment, and then you will finally get a visit with the doctor. By that time, the diseases progressed and worse is nine of the time, you know, there's not much to do. They'll tell you to sleep it off, right, if you haven't already done that already, right, So this whole process is inefficient on both ends of the spectrum. It doesn't get to the people who need it early enough, and then it wastes way too much energy looking at patients that actually don't. And if I can and this is really the nuther bit it wastes way too much energy and waste too much money. Because what you're describing is you're you're describing that the customer is the government. The traditional OPM remote patient monitoring and healthcare companies are selling to the clinician persuade the clinition. But you're talking about environment where you say to the government, look, if you go from reactive to productive, preemptive or continuous monitoring, you're actually going to say potentially hundreds of millions, maybe billions of dollars in your national healthcare infrastructure because...'s just a lot of them aren't going to go in the first place, they're going to have an intervention. So actually you're selling at the government level, that's right, medical device for the population. That's right, that's right, that's exactly correct. Yeah, and it's just a safety mechanism exactly to your point for those countries that have nationalized health systems, right, it is a way to achieve a tremendously higher efficiency in terms of allocation of resources. Right. You are then HS, you are the Ministry of Health and Singapore, and you're trying to figure out which patients to allocate your resources to. Something like this can be essentially in destructive and game changing because we are bringing comptinguous monitoring to the table at this point in time. Yeah, and wow, tremendous and that model when you think about the potential globally for that model, it's truly disruptive in a very good way. And as you said, it's not disease specific. I think he used to phrase early on it quite like that. The AI m L doesn't know it's stage an interpretation. It could be a heart condition, but it could go across so many different areas. And you guys are one of the first movers, you've raised the most money, and so clearly you know you're pushing hard. And I know working with you on a on at daily basis where we are working on a daily basis, you guys are running hard, charging hard in many different areas. And it's such an exciting IoT case study, which is what these podcasts are all about. And I think it's a tremendous story. Let's finish it, we can. We could go on for hours on this, but let's finish it. We can. Is there a vision? Is there? It's so big, it's so you think how you could do this, it could do that, you could do this. Is there some vision for how it just all becomes part of our daily lives? You know? Yes, Nick, you know the recently I was talking to someone and a sort of an analogy came to mind, right, which is if you think about just you know, fifteen twenty years ago, maybe right, GPS was a new thing and you went off and bought this, you know, two thousand dollar pieces of equipment, you know that sat on a big sack on your car dashboard type of thing. GPS has now become completely ubiquitous, either your car has it or you're certainly your phone has it. And today, while of course it's entirely possible to you know, to transport yourself in cars and automobiles without a GPS, practically nobody does it, right, everybody, you will, even on your daily commute you want to take a look at the best route given all the trafficlution dynamic. It's more intelligent than it's absolutely, yeah, yeah, exactly, and it's really no cost. It's right there. It's something that has a tremendous benefit and really not much downside to it at all, and everybody uses it, right, It's almost impossible to think about landing or trying to go somewhere without a GPS these days. Yeah, Well, the same analogy applies to healthcare. Whether you're navigating a disease or you are prescribed a complex, molecular, complex regime of medication, trying to navigate that journey completely blind with no guide at all just will seem very very antiquated. In a very short order, systems like ours are likely to become very ubiquitous. People will just have it, whether it is because you know, the Government of Singapore just gave you a device and it's just easy enough to wear it, and now you're navigating your health without really having to do much about it. You know, just wear a device and and and and the system will tell you when you might need to get some care. Or as we described, you know you're in some complex medication regiment, then you have a system like ours that is guiding you through through that journey as a patient. I think systems like ours are going to become completely ubiquitous. It will be kind of uh impossible to think about the day and age when we were trying to navigate personal health absolutely no guidance, Like you're saying on the basis of of a little sheet of paper that was folded together by an Oregon the expert, right, I mean that's the apple those which nobody reads, very few do.

After I've read I've read a few and then I'm not going to read them to scare. But you know, they do say that that technology truly becomes ubiquitous when it's in effect invisible and thought of what you're you're describing it will be you notice it more by its absence than by its presence, and that's exactly what you're describing. So Melan, I think we're going to have to leave it there. It is such an exciting story on multiple fronts. I mean, it brings all the pieces of IoT together, but it also addresses such a big problem with such a huge potential opportunity. I mean selling to governments. Like you said that, the government funds the device, so it's a different commercial model, something that's transposable across different human conditions, the pharmaceuticals and the healthcare providers being different groups. It's a really exciting thing and we've been very happy with our partnership. As you say, everybody always thinks the device is easy, and every single one of these podcasts is sort of clustered around that central problem. I thought the device was easy and then I found out it wasn't, so came to s I, so thanks to that. I know there's lots of exciting things in the future which we can't talk about, but in the meantime, it's been a really, really great podcast. Thanks for joining me, and I'm sure our listeners would have loved listen to it. So thanks for being my guest on the iote Leads podcast, and Nick, thank you for inviting us. Really appreciate the time. Thanks for tuning in to IoT Leaders, a podcast brought to you by s I. Our team delivers innovative global IoT cellular connectivity solutions that just work, helping our customers deploy differentiated experiences and disrupt their markets. Learn more at SI dot com. You've been listening to IoT Leaders, featuring digitization leadership on the front lines of IoT. Our vision for this podcast is to be your guide to IoT and digital disruption, how hoping you to plot the right route to success. We hope today's lessons, stories, strategies and insights have changed your vision biot. Let us know how we're doing by subscribing, rating, reviewing, and recommending us. Thanks for listening until next time.

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