How New Technology Can Make Wearable Medical Devices and Smart Watches Smarter
Wearable devices have become common for a number of applications – from fitness tracking to tracking medical parameters for people with conditions like diabetes or heart arrhythmias. The overlap between consumer and regulated medical device wearables is becoming very fuzzy, but has a bright future. In this episode, Andrew Rickman, the CEO of Rockley Photonics, discusses why wearables have taken off, the differences in consumer and medical wearables, how multi-parameter data can lead to better healthcare, the benefits of longitudinal data, the products Rockley is introducing, how medical device companies may benefit from this emerging technology, and the future of wearables.
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Episode Transcript
This transcript was generated using an automated transcription service and is minimally edited. Please forgive the mistakes contained within it.
Patrick Kothe 00:31
Welcome! Wearable devices had become common for a number of applications. From fitness tracking to tracking medical parameters for people with conditions like diabetes or heart arrhythmias. The overlap between consumer and regulated medical device wearables is becoming quite fuzzy. The ability to use this data is dependent on the tech not on the technology itself, and understanding what the data actually means. Our guest today is Andrew Rickman, the CEO of Rockley Photonics, a company that's developing next generation technology that will be used in your smartwatch as well as in dedicated medical devices. Their technology will expand the number of parameters that can be managed, potentially opening up new insights into disease identification and health monitoring. Andrew has a fascinating background as he was Britain's first internet billionaire, and then a survivor of the.com bubble crash. He's an expert in silicon photonics and as commercialize technology and fiber optics, communications, high performance computing, sensing applications, and now medical technology. He's founded and sold companies in the space and has received numerous awards and honorary degrees for his business, engineering, and leadership skills and dedication. In this episode, we discussed why wearables have taken off the differences and consumer and medical wearables how multiparameter data can lead to better health care, the benefits of longitudinal data, the products Rockley is introducing how medical device companies may benefit from this emerging technology, and the future of wearables. Here's our conversation. Andrew, thank you so much for joining us. It's a pleasure, Pat. Andrew smartwatches, wearable fitness devices and health trackers seem to have really exploded over the past five to 10 years, I saw one report that the global smartwatch market was valued at about 70 million units in 2020, and is expected to reach 230 million units by 2026. You may have more accurate data than me. But why is this happening? Why are so many people gravitating towards these devices? And more importantly, what are consumer expectations of these devices?
Andrew Rickman 03:17
Well, of course, these devices, smartwatch smartwatches have more functions than just monitoring your, your health. But I think we're all more interested in understanding what's going on with our health. And these devices provide you with real time tracking information. And you can look at your look at the apps on your phone, and you can see what's been going on and you can set targets. And I think I think everybody's seeing the benefit of that. And then of course, the the medical world is also starting to see the benefits of continually monitoring, biomarkers, and tracking those over the course of time and what kind of diagnostic information that they can they can provide and how that they, they can basically lead to preventative medicine.
Patrick Kothe 04:06
So when these devices first started coming out is basically you know, step tracking your steps. And as time has gone on, more and more parameters are starting starting to show up. So do you think that that was due to the need for it? Or is that just technology that people thought was interesting that could be interesting to consumers as well?
Andrew Rickman 04:31
Well, I think if you if you can measure a significant range of key biomarkers on a continuous basis, it sort of becomes exponentially more valuable from understanding an individual's health state unit for every every biomarker that you add. So yeah, I think there's a great push to try and add additional biomarkers. If we look at what we we see today it's really very rudimentary. type of measurement technology, using LEDs to measure your heart rate, heart rate variability, breath rate, accelerometers in there for your steps and other other important functions. blood oxygen is measured, measured using a red LED and an infrared LED. And, of course, things like ECG electrodes on wearables, so they, you know, those are all, today fairly inexpensive, fairly kind of rudimentary technology, that that can be implemented. But to expand out the biomarkers that I've just mentioned, from where we are with, with that level of measurement technology is very, very, very difficult. So something new needs to come along. In order to expand out into convenient and accurate blood measurements or blood pressure measurements, you know, accurately measure your core body temperature from from an extremity hydration, and then looking at things like metabolites like lactate, and glucose. You need, you need technologies that deliver performance that are many, many orders of magnitude higher than what you see in wearables today.
Patrick Kothe 06:21
And I really look look forward to getting into further into the discussion, what you're doing at Rocklea. And the technology that you're, you're delivering there. But before we get there, what have we learned about what consumers want? And what what these existing parameters that are delivering what have we learned about that about about those consumers and what they want from from these wearables?
Andrew Rickman 06:48
Well, they're able to track their, their state of health more accurately able to set targets. And you're able to see that, you know, that they can make tangible improvements in terms of their health, by, you know, taking the feedback from from the information that they receive. And I'm working, it's always hard for, for us all, but working to change one's lifestyle, to improve health.
Patrick Kothe 07:17
So we're talking about health. And then there's a medical application for those. And there's a transition between a consumer type of device and then medical devices. And I want to explore that a little bit and find out from you what kind of how you view that and how you view that transition and going from a nice to know information. Two things that you can help to really manage a disease state or monitoring a disease state or identifying a change in your health so that it is more of a medical device, as opposed as opposed to a consumer and looking at how healthy or in shape you can be. Yes,
Andrew Rickman 08:05
well, I think the medical profession is bringing all of this alive. I mean, for example, in let's say, managing somebody with type one diabetes. Medical professionals basically are very keen to understand from their patients. Key biomarker information that those patients have tracked over time, not not just what they're getting from their possibly their their CGM, their continuous glucose monitoring devices. But to track other things, you know, the temperature, the blood pressure, there are other chemical biomarkers metabolite biomarkers like lactate that are important. And what they've figured out is that if you look at all of those biomarkers, and track them all together, that over you can see how a patient is progressing with the management of their of their disease, whether it's things are improving over time, or declining over time. And folks are developing algorithms based upon that longitudinal information that can crunch all that information and very quickly give the the physician the guidance that they need in order to advise the patient and for that matter for the for the patient to see that as well. So, it that's all going on at the moment. But the problem is that some of the biomarkers that are mentioned there, you know, they don't exist in it conveniently and wearables and things like you know, glucose monitoring is still you got to stick a pin in your arm it's has a certain level of inconvenience. devices only work for a matter of, you know, one to two weeks before you have to replace them. So being able to it's already seen what the advantages in terms of tracking a range of biomarkers but just making that so much easier. It is really the, the opportunity. And so when you look at the consumer wearable, today, you've got some of the biomarkers, but you're missing some major ones that are needed in order to implement and sort of automate that, that medical model. So this, this is a collision, if you like, between what has been demonstrated and what is there in real in the consumer world, and the desire of the medical profession, to see patients being able to monitor their key biomarkers on a continuous basis, and the longitudinal algorithms becoming available to actually make sense of that information.
Patrick Kothe 10:45
I think that, as you explain longer term monitoring, I think we've all kind of had the experience with with, you're going to the physician and say, Geez, you know, My knee hurts when I do this, but it's not, it's not the acting up today, or you take a car, and it's making this sound, but not today. So being able to monitor over a period of time, you're going to catch some of those things. In the cardiac space, people were having heart arrhythmias, and you started wearing a big, basically a recorder, looking at your ECG for a 24 hour period, holter monitor. And then if it was outside of that window, then people so we're gonna put a vent recorder on there for 30 days. And we'll track somebody for 30 days and trigger when when an arrhythmia comes on. And that became a interesting thing. What we're talking about here is longer term monitoring to catch everything. But it's not only catching the events, but it's looking at trends and looking at things further away. And I'm at a baseline and I'm getting away from a baseline and what does that mean, they still may need to go in for a definitive testing. But this gives gives a good indication that you need to make some type of intervention. So when you're looking at this, this tracking of this information, part of this is with the consumer and what the consumer gets out of it. But the other part is what the clinician and what the healthcare team gets out of it. So from a consumer standpoint, you can say, Okay, I'm managing my own health, and I see that my glucose is down, or I see that I need to hydrate and you can you can take an action, but with this massive, massive amount of data, it's not always good for the clinician to get this massive, massive amount of data. So how do you how do you separate the noise from from something that's valuable?
Andrew Rickman 12:51
Well, that that is where one does need to develop what we call longitudinal algorithms are essentially applications that basically take this continued monitor data, and crunch it into a form that is, is comprehensible and understandable and actionable. And when you look at it, you know, it's something that doesn't take, you know, hours of a physician's time to comprehend it, you know, typical consultation is only, you know, 1015 minutes, that it that it's got to deliver the information and the advice in an effective way. Now, how we see with our with our partners, because our world is basically in the, in the development of the of the platform for collecting biomarkers, but as we look at our partners, looking at the, at these longitudinal algorithms, and basically taking the data and turn it into sort of actions or actionable information, AI is generally based upon human studies. So I looking at the data, and looking at the patients in human studies, and correlating the trends in that data to the state, and the trends associated with the, you know, the health of those individuals. And that's the that's the method or a method of progressively developing better algorithms, better applications that can crunch otherwise impossible level of data that that nobody's got time to understand.
Patrick Kothe 14:29
Let's talk about precision for a minute because it's an interesting thing. We in the medical device field, we believe that we need to make very precise instruments to enable us to see what's what's going on. But the need is a little bit different here because the precision you may not need to know that you know 121 over 80 It could be 120 over 80 versus 131 versus 130. So the precision of of the measurement may not be as impressive As the trend of the measurement, can you talk to me a little bit about precision and kind of how you look at that for this type of an application?
Andrew Rickman 15:10
I think if we look at wearable technology today, it's based on generally on LED technology and fairly rudimentary electronics principles on. So you know, there are challenges using that technology to be able to deliver the kind of precision that you would see from medical device, you also seeing a world which is is not obviously not heavily regulated? And exactly what people are doing in their algorithms with that data in in wearable devices is a bit of a black box. And you can you can, you know, you can fall those sensors and devices quite easily. So, yeah, there's useful longitudinal data. But yeah, there are definitely limitations. People talk about, you know, extracting more information using algorithms, and algorithmic techniques, and, you know, artificial intelligence. But at the end of the day, if the data that you're gathering is of only a level of precision, and you only have a certain amount of information in there, it doesn't matter how, how much how many clever techniques you throw at it, you're just going to be limited by that the quality of that data. So yes, I mean, if you can, if you can truly take the gold standard measurement techniques out of the hospital and the clinic and put that on your wrist, then, you know, then you get the benefit of both those gold standard measurements, but you get them on a continuous basis. So I, you know, from where I stand to an extent, you don't need to compromise on that particular point,
Patrick Kothe 17:07
you're going to try and be as accurate as possible, but a clinical level, give me an example, ECG, we can do on on watches now. But it's only a one lead ECG. The definitive study is going to be a 12, lead ECG, to enable you to see all the way around the heart. So you may be able to see some rhythm information with a with a one lead ECG, but you're not going to be able to really diagnose where a heart attack occurred, or many other things that you would have have to look at when you when you analyze a 12 lead. That's not to say one lead is is not important. It's important and may be important for tracking rhythms, but it may not be as as different, it is not as definitive as a 12 lead ECG is, so the level of precision doesn't necessarily need to be the same level, as long as your claims of what you're trying to track are well understood.
Andrew Rickman 18:07
I think that's true part. But I think there's another factor here as well. And that is we are, we do think about, you know, the individual biomarkers and techniques are based upon the measurement science that we have, you know, let's say within the hospital. But what if you you move that measurement science on then you you land up gaining other information that maybe you didn't have before, or information that is not in the in the same format, that the the profession is used to. Now, that's a fairly dangerous thing to say, because we know that the medical profession is very cautious and takes a long time to adopt things. But that is the reality. If we can bring really, really powerful sensing technology into wearables, then you you will end up gaining information and insight, which may step outside what we're used to in terms of standard techniques and biomarkers. But in stepping outside those the information may in the longer term turn out to be incredibly profound. So for example, to your point about, you know, 12, lead ECG as opposed to two, I maybe there are other ways to getting to localizing and identifying a particular cardiovascular problem that are that are, you know, not limited by a whole load of leads all over your body.
Patrick Kothe 19:44
And I think the other thing that you said in we've got a certain number of parameters, biomarkers that we're tracking right now and as we add more in there, now you start looking at correlations is the SP co2 going up or down? Is temperature going going up or down his heart rate, heart rate variability, is that becoming stochastic? So all of these different things and you start correlating If This Then That, then it becomes a marker for something that doesn't exist before. It's so it's better understanding all of the data that you have access to. Is that is that? Is that kind of where you're going?
Andrew Rickman 20:20
I think. I think that's a great, I think that's a great point. I mean, there's there's two elements actually, in this discussion, one, one is having more powerful continuous monitoring techniques that deliver information that is not in a standard format, if you like, but but can have very profound information, but may take time for people to adopt. And then the other one that you you say, I think is a great point. And that is that, you know, generally diseases, we're looking for a key single biomarker, that characteristic characterizes that disease, for example, a, a particular biomarker that you can identify, let's say, in a blood draw. It's more, you know, obviously more difficult to to, to do that in a real life situation on a continuous basis. So absolutely, the replacement for one definitive biomarker can be a group of biomarkers which collectively together in terms of their behavior then correlate accurately to a particular disease state.
Patrick Kothe 21:33
So let's kind of discuss what what Rockley Photonics is, what the technology is all about. And just give us a little bit of understanding about what what this technology does.
Andrew Rickman 21:47
Thank you know, going back about almost 20 years now, several groups started to look at the idea of using infrared spectroscopy shining light into your body, and looking at the diffuse reflected light that comes back and analyzing the wavelengths associated with that. So really taking the principle of used for things like pulse oximetry, where you're using typically a couple of LEDs, taking that much further into a longer wavelength region with much higher spectral resolution. And looking into the body in spectral regions where where they're rich in a biomarker signatures, spectral signatures, you know, for example, glucose, or lactate, or urea. And so that that work led to the development of, or the adaption of, of expensive benchtop spectrometer instruments that demonstrated that you can measure an awful lot of things non invasively, using that approach, and that's very, very exciting. I mean, one of the companies that pioneered that area was a company called True Touch. And True Touch is now part of Rocklin photonics. So the obvious challenge was that the as a benchtop, instrument, measuring a lot of things well, but where there were other techniques, I mean, if you wanted to measure glucose, you didn't need $100,000 spectrometer to be able to do that, just pick your electrochemical process, and, you know, prick your finger or now, now, these continuous monitoring devices. So that is a practical system really didn't, didn't change anything. But the obvious thought that came into people's minds was what if you could miniaturize that instrument. And the challenge was that when you try and miniaturize a large, laboratory based chemical spectrometer, that you make the lamp that generates a broad spectrum of light that you need smaller. And as the light goes into the skin and is scattered back, the aperture that collects the light has to get smaller as well, because you're making the whole thing smaller. And the result of those two items becoming smaller is that you have a significantly reduced signal to noise ratio. So by the time you managed to move it onto your wrist, and you know, it's completely useless. So in the case of of Rockley, we've been working. This is our third company. We started in 2013. Working on in the field of silicon photonics, we founded the first company in Silicon Photonics. And we were backed by back then by amongst other Intel Gordon Moore was involved. We had Jack Kilby on the board of that company that kind of gives you some idea where where people think, you know, Silicon Photonics might go, it's been a long journey. And we had two previously successful companies, this is our our third company. But we developed a completely unique form of silicon photonics, which is absolutely ideal in building something called a spectrophotometer. And a spectrophotometer is turning the problem around, it's basically, instead of a broad spectrum of light being used to illuminate what you're trying to analyze, you use individual laser wavelengths, and you turn each one of these wavelengths on very fast. And you put all of the safe optical energy into that wavelength and it goes into the body and scatters back. So each spectral line that you measure now has actually a higher signal to noise ratio than their original benchtop instrument. So the principle overcomes the problem. And, of course, the need is to have developed a semiconductor process that can do that, that can make essentially a chip that has the ability to generate laser light, very accurately over a, you know, a spectral range, that's at least three times that the visible spectrum. And you know, that's what Rocklea has done, we started in 2013. It's cost many hundreds of millions of dollars. But that production processes now in a volume, foundry ecosystem, it's our process technology. And we've taken that process technology, and we've built spectrophotometers, and we built them into wearables, and we've conducted human trials and against gold standards, across multiple biomarkers, and develop the algorithms. And we, we it's extremely exciting. And we are in the process of moving towards mass production of, of this technology, which will be introduced in the fourth quarter of this year. And, you know, we have, you know, the world kind of leading customers in the wearable space, the consumer wearables space, who are fascinated with this, and you know, wanting to use this technology. And on the other side, we have the world leading medical device companies as well as customers.
Patrick Kothe 27:37
So let's talk a little bit about current state. So you mentioned that current technology is LED technology. So whether you've got, you know, whatever brand of of smartwatch that you're wearing is using is using an LED type technology right now. Exactly. Yes. Yeah. And are those technologies? So as the companies that market market is the smartwatches? Is that their own technology? Are they buying chips? Are they buying components from other manufacturers of those of those devices?
Andrew Rickman 28:12
Yeah, there are a couple of key suppliers out there, it's, it's very broadly available technology. So essentially, everybody is using the same thing, there's no significant hardware differentiation associated with it. With that it's become in terms of the hardware, it's become completely commoditized.
Patrick Kothe 28:35
So your hardware would replace what's in those available devices today,
Andrew Rickman 28:41
it actually complements them because in the visible spectrum, where the LEDs work, the thing that dominates the optical signal that goes into you know, that you shine into your body is hemoglobin, that's the thing that that dominates. And so, you know, the pulse oximeter that was invented in what was it 1971 really started all of this with LEDs, where if you have a red LED and infrared LED, very near infrared LED, then you're able to see the ratio of absorption of oxygenated hemoglobin to an oxygenated hemoglobin. And it works well. In the case of the green LED, which measures your heart rate reasonably well. And the reason that green is used is that is a peak of hemoglobin absorption. So as your as your blood vessels expand and contract and the volume of hemoglobin effectively in the optical path is increased and as expands it and decreases your blood vessels as blood vessels contract and that's how that those sensors work. So we haven't replaced those. We've implemented those as well and in the in the consumer wearable space, our customers happy to use what they already have there. The area that we've opened up is basically into deeper into the infrared, where the signature signals associated with things like lactate, or glucose, or alcohol, or measuring hydration or measuring your core body temperature, the signatures associated with all of these things are much, much stronger. And you need, they're still, they're still small compared to the hemoglobin signal. And you need the lasers, laser technology as opposed to the LED technology to be able to detect detect them. So that that basically complementary techniques.
Patrick Kothe 30:40
So I manufacture both both technologies with within their device,
Andrew Rickman 30:45
yes, if you look at the back of one of our devices, what you'll see is an a number of little apertures that are associated with the LEDs, and then you'll see a number of little apertures associated with the the infrared spectrophotometer, where the light is coming out, and then and then the scattered light has been received by the device.
Patrick Kothe 31:05
Is your business model to sell this product to the manufacturers? Do you have plans to bring your own device out? What's the business model look like? Um,
Andrew Rickman 31:15
the business models, I think it's interesting. I mean, one of the things if you come at this from a background in the semiconductor industry, which some of us do is that, you know, the dream is always to come up with a semiconductor process that has significant barriers to other people entering anytime soon. And that's when you qualify a semiconductor process, especially one like ours, which really bears no resemblance to anything else. It's, it's a many, many year process for others to be able to replicate and qualify that and get it working, even if you give them a manual. And even if they get round your IP, it's still a big big barrier. So when you've got something like that, you you don't just want to sell the chips for, you know, a kind of margin plus on what they cost you, you want to build as much value on top of that innovation as you possibly can. So in the med tech space, we set out to build the wearable, ourself, and in that type of wearable for the medical space is not like a consumer wearable, you want the battery to last for a lot longer, you don't want it to necessarily be a telephone, and, you know, send your emails, you want it to sit in the background. And just continually monitor and stream that data. You know, in the case of using in a hospital into the hospital, wireless system, network, continuous patient monitoring, remote patient monitoring, obviously, through something like a gateway or phone into the cloud. So what we did was we built all the hardware, we built everything we built, we've now got a device that's going into manufacture which is easily cleaned, it's very suitable for a hospital environment, it's also very suitable just to sit and sit in the background for anybody to wear. And we built the the software platform that very conveniently strange all that data into a data lake and then creates, we've created the environment. Because we have very special data. This is spectroscopic data in its raw form. And then we created the tools in the cloud, to allow ourselves and our customers and our partners, anybody to be able to interrogate that data and conduct human trials and develop what we discussed earlier, essentially longitudinal algorithms or applications that basically continually search through that data and provide the individual and their health providers with alerts, advice, potential diagnostic tool, providing a prognosis, so on. And so
Patrick Kothe 34:11
this is really fascinating, because if you've got a consumer device, and you're partnering, and you're partnering with the large, large people who make smartwatches, we all know who they are, that data is going to be coming in to that company. Basically, if you've got your own device that data is coming into you. So I'm curious about the development of these algorithms. Because if the data is owned by the smartphone manufacturer, when I say owned I mean anonymized data that could be used from a research standpoint, if the all of that is coming into that smartphone manufacturer, they control who has access to it and And they control you what algorithms would be would be developed? Is that correct?
Andrew Rickman 35:08
Well, I mean, it's the patient who owns or the individual who owns the data, but what the, it's what the consumer device company does with that data in a sense. And so when you, when you take the consumer device devices, and you put them into, you know, clinical environment, then it's very frustrating for for the folks researching in that environment, because they really just want to get to the root of the data. And they don't want a lot of stuff in between the is supposedly, you know, the differentiation or secret sauce for that company, they just want the data
Patrick Kothe 35:43
where I'm going with this question, it goes to development of the algorithms of multiple parameters. So if you've got multiple parameters on that consumer device, who is going to be able to look at that data to say, I'm going to build an algorithm to look for Parkinson's, or I'm going to develop an algorithm to do that with your own device. We'll get to that in a second. But I'm curious on the mass mass market, who who has access to that?
Andrew Rickman 36:14
Yeah, so the one route to market that we have and and the customers that we have are in the med tech field, and they range from medical device companies all the way through to health providers, and there's no restriction if you like, on the data, we're not trying to block anything from anybody. And they, they're free to get on and develop the algorithms, you know, without any any constraint whatsoever. And we sit there with the benefit of obviously selling our devices, and selling the licenses to the software platform that allows them to develop whatever it is that they want on top of it. So it's much much freer environment than you would see with a consumer wearables today. And we're able to do that to the hardware differentiation, in the consumer wearables space, we are working with what you would call the tier ones and the tier twos in the consumer wearables space. And, you know, with those, those businesses, they want to incorporate these key biomarkers into their devices, but they are consumer devices, they, they do other things, they you know, smartwatch does a lot of other things. So they're not necessarily the same profound medical device, that you would see it starting at it from the med tech space. So there really, if you'd like to coming out the problem into two different ways, if you really want to get profound data, and monitor it on a continuous basis, you you really need to have a device that dedicated to that task. Otherwise, you know, every every day, you're charging the battery up, and you're limiting the sample rate that you can within the device. So there are really two different spaces. And I think that the med tech space will develop algorithms and develop more profound, valuable things for healthcare in the consumer area. And I think it's going to be it's a wonderful opportunity and creates, you know, a mass adoption. But the format is different. And the access, that those those customers using our technology, or will provide to their data will be will be different and will not be entirely in our control.
Patrick Kothe 38:33
But you may be able to utilize that data that you're or the that learning that you've got on with the medical device back to the consumer space, once you learn what those algorithms need to be.
Andrew Rickman 38:44
So you know, the consumer customer may have that completely their own cloud infrastructure. But But installing algorithms that we along with tunable algorithms that we've developed, or partners that have developed such as one of our partners is Mount Sinai Hospital System. There's very, very savvy in this area and very experienced with using wearable devices and the data associated with them, that those those longitudinal algorithms, as you say, let's say for early diagnosis or treatment of Alzheimer's, and there's no reason commercially why they won't find their way into the consumer device, consumer device companies, ecosystem and offerings
Patrick Kothe 39:30
as a consumer device technology, exactly the same as what you will have on your medical device technology.
Andrew Rickman 39:38
Yes, yes. With the with the restriction that if you're powering something that is, you know, a telephone and telling you emails and doing a lot of other things that you you essentially are going to facilitate. There's going to be a different kind of duty side A call associated with that device. And if you just take, for example, a wearable today, your if you turn the blood oxygen function on in a smartwatch today and you turn it on, and you want it to measure literally continually, then the battery will run flat flat in perhaps an hour. So, um, you know, that's not, that's not great. Fortunately, in our technology, that in, we're putting about the same optical energy into the skin at optical power to be precise, but we only have to do it for a very short period of time. So actually, our duty cycle, just to take a measurement is much less for all these extended biomarkers, but still, we, you know, we don't want in the med tech space, we don't want to be restricted, you know, to limit the duty cycle, if it's going to be beneficial for the human trials that have been conducted the particular thing that folks are looking into, to sample very, very regularly, then we don't want that, you know, we don't want the device is not going to provide the same restriction as a standard consumer device would.
Patrick Kothe 41:11
And really, the key is the understanding, and it's built building those algorithms, because the technology is gonna get better, I mean, the battery life is going to be greater technology limitations, will be different as you go forward. So right now, Apple Watch seven, you hear about ECG and arrhythmia, and a fib monitoring, well, they don't monitor every every beat damp, they're sampling for a period of time. So they're gonna be they're gonna catch some but not all the fascinating
Andrew Rickman 41:42
area, because we did put out a press release on this that with laser technology, one of the things is not just spectroscopy, one of the things you're able to do is measure blood flow very, very accurately. And we get a tremendously high fidelity blood flow signal. And within that blood flow signal, it we have found very strong correlation to, to blood pressure. And so that's, that's, that's one thing that we've we've stated. The other thing is that we found very strong correlation to the ECG signal. So now you've got something where you don't need any electrodes, you've got something which is sits there and could continually monitor. And, you know, in our, in our subjects, we found, for example, we don't notice the sinus arrhythmia, from that signal, and you can see it clearly both in the ECG signal and in our blood flow signal. But in that blood flow signal, this is coming back to the point we were talking about earlier, we see the big peak, if you like of the of the blood flow as your heartbeats and we can see your aorta Valve are closing, and then lot of other little little blips before the next big beat. And those little blips are basically signals from or reflections from within the arterial system. And so what people are going to discover with that, you know, maybe that maybe that's sinus arrhythmia that actually the location of the associated problem, what is actually in that, in that blood flow? Data, I mean, it's going to be exciting to, to pursue that, that reserve research direction. And that's what I was saying that, you know, ECG has a great gold standard. In the wearable space it has, it has limitations that we talked about. But here we come along with something that that looks like it's as good but actually has more information and isn't, isn't restricted by only be able to monitor for some of the time.
Patrick Kothe 43:57
So those of us in the medical device area, we talk about medical device, but we'll say the regulated medical device area, there's a lot of medical devices, but there are certain that are said to be regulated medical devices in the consumer space. These are consumer devices, they don't make the claims of diagnosing or monitoring certain things. So their true consumer devices, the medical device that you're manufacturing, is that a regulated medical device? Or is that still a non regulated device?
Andrew Rickman 44:36
It's still unregulated today, but it is our intention. In fact, we put out a press release with Medtronic that we'll be working with Medtronic on regulated versions. And so what does that mean? It basically means going through the clinical trials that are necessary to get approval from the FDA for each individual by biomarker, so you actually, it's a sort of a graded area, you can see a wearable, it can be FDA, clinically approved. But that might only be one biomarker. And so what will happen with us in the course of next year is that we see, we'll see, we believe a number of the biomarkers get FDA approval, and then, you know, and then eventually, all of the biomarkers that that we're producing will get approval. Now, what we've done so far is that all of our human studies that we we've used, our wearables on so far are essentially the same thing as the clinical studies that we will need to go through for FDA approval, but we use those human studies to develop the biomarker algorithms. And to validate the technology against the gold standards. For the future, it's essentially running the same thing, but instead of foot playing around with the algorithms, and and you know, tweaking the technology, the hardware is fixed, it's the production version, the algorithms are fixed, and then you get get to FDA qualification. But there is what we, what we what we thought, even just a year ago was that really, nobody would use this in the medical field until it was in so somebody was FDA approved. But actually, that's not the case that that CROs, for example, one of monitor all of their subjects in human injury in drug human trials, that they're okay with this not being regulated, monitoring the elderly, just, you know, monitoring patients full stop, you can't You're not been delivered information that you can directly take action on, if you like, like, like administer a drug or something. But you it is support information that many medical professionals have already recognized as, as as very valuable. And that that value just increases as you get to FDA approval.
Patrick Kothe 47:12
So you mentioned in q1, you had a press release, you wanted to 2022, you had a press release about a relationship with Medtronic. And I also noticed in that, in that release, you have many other medical device companies that that you're working with right now. What are the what are the nature of those relationships? And what are medic? You know, how to medical device companies get benefit from this technology?
Andrew Rickman 47:44
Great question. Um, so talking in general terms, what our what our business model is in the med tech area is that med tech companies not so orientated towards building, you know, wearables. And we built a great wearable for their particular application space. So they're very happy, or our customers, they're very happy to take what we're producing in terms of the hardware we don't, it's not a question of providing them with the chips if you like, and they build the hardware, we build the hardware, we build the stack, and we deliver the data into his essentially a cloud environment for them. So we can manage, recreate all the software to manage those wearables across patients, and deliver the data and to provide those customers with the tools where they can build apps that specialize that wearable device. So that specialism could be, let's say, in inpatient monitoring, so if somebody comes into a hospital, the beginning of their hospital journey, they have one of these devices put on them. And it's continually monitoring them, it's streaming the data into the hospital cloud. That information is basically you know, it's got got the biomarkers have got guard bands around them, those guard bands really go out the Guard Band, there's, you know, the the medical staff are alerted, you're reducing the amount of time that nurses need to spend taking measurements of patients because you've got a continuous monitoring process. You can you can see all the software that's built on top of that, that's not our not not our, we can't tackle everything. And so that's one application space where customers are pursuing the development of the software on top of our platform. And it just goes on like that if we go to your specific disease state, so if you want to monitor if you want to interrogate the data, for managing better managing, let's say a diabetic, then you have that Start, again is another application. So the customization for these companies is based on on software, not on hardware. Now that our applications for the for the for the photonics directly in medical instruments, and you know, but these are essentially much higher value, much lower volume type applications where there's much more specific engineering kind of work going on. And we're not tackling those opportunities today, we will tackle those in the longer term,
Patrick Kothe 50:33
I would see with medical device companies that could be at the top of the funnel to bring people in to identify different diseases or different issues that they're having to be able to get them into treatment, and then monitoring as you sit in the hospital. But it's also post procedural a lot of devices post procedure and monitoring that patient post procedure, because that's outcomes, and better outcomes are better for all of us, device companies, physicians and patients. So being able to monitor them post procedure, I think, would be a really interesting space to
Andrew Rickman 51:10
the idea of, of early diagnosis, which means better outcomes for patients, because they get treatment earlier, less expensive, less less of a load on the healthcare system. And then absolutely, sending patients home earlier, from hospital freeing up hospital beds, with the knowledge that you can call that patient back in, you know, very, very quickly, in the event that you see their biomarkers moving out of the norm. I mean, something for example, that comes on obviously, very quickly, like, like sepsis. You know, lactate, is one of the key biomarkers in there to monitor. And so be able to react quickly to that is, is extremely important. That's just one example.
Patrick Kothe 52:00
Well, this has just been a fascinating conversation, I really appreciate you spending time with us kind of going through all of these issues. I think that we've seen that there has been this explosion in wearables and a technology like yours, being able to expand the number of parameters, and then have the ability to look at that data and you sharing that data without licensing or without charging for that, that is a really a huge benefit to the patients and to the medical industry. And I really, really compliment you for the steps that you're taking along those lines.
Andrew Rickman 52:39
Thank you. Yeah, I think opening up within, you know, the confines of confidentiality of people's data. Using that data, basically, with our partners, to be able to create the longitudinal algorithms, that's where the value comes from that data, you're essentially learning from the data, and that learning and that knowledge is then implemented into the algorithms that then just sit there in the background, looking at all future data to spot what's wrong, you know, and, and potentially, as you say, create better outcomes for people.
Patrick Kothe 53:20
So future, let's talk about the future for a second, what do you see the future of wearables being? And how is that from the consumer space to the medical space? How are how is that merging together? And what does the future look like?
Andrew Rickman 53:35
Well, I think it's, I think the future is precision medicine, it is all about continually monitoring, you know, your health, just like you know, all the instruments on a car or a plane or something like that, that you know what's going on. And all the benefits that we've discussed associated with that. So that's, that's how we see it playing out, it just becomes a natural thing that people will have sitting in the background, whether that technology sits in a dedicated device doesn't necessarily actually need to be on the wrist, it can be on the on the upper arm or other locations, but just out of the way, that's one kind of format of it. The other format is that it obviously in a in a somewhat compromised approach is sits in in another device with other functions and monitors as well. So those are the two different kinds of formats of the device, I think for the future. It's extraordinary potential in terms of turning what what I think the world is to to doing today which is not healthcare, it's sick care. You know, we're looking after people who've become ill and and if we can identify where lifestyle and things are going wrong much much earlier. Then the The change in lifestyle, early treatment can have a huge and profound impact on everybody's health.
Patrick Kothe 55:09
The wearable space is rapidly finding its footing and may look different in the coming years, technology developed and better understanding of the data is going to drive where it goes. A few of my takeaways, first old opinions on the value of wearables is changing rapidly. It's not just a toy that medical device folks ignore. Please notice the relationship that Rocklea is now established with Medtronic and other medical device companies for hardcore medical devices. Second, backward integration from dedicated medical device to the consumer devices, I thought that this was a very interesting comment on how you can leverage the data that comes off of these devices and and and how you can build the software and the algorithms in the dedicated space and then back integrated into the consumer space because the hardware, it may be identical to the consumer space. It's just the know how and the learning that you've gotten from the from the dedicated medical device space. And as always watch the claims that are being made for each one of those, those types of devices. Finally, Andrew comes at the problem from a non medical background. You heard his background in terms of engineering and different types of technologies. And now he's in the medical space. So he's got deep domain expertise, but he understands the technology, not necessarily the application. So he's opening up the data to his partners, and companies to be able to dig in and understand and develop the hardcore medical insights from the technology. That's, that's he that he's providing. So it's really opening up the data for partners to gain the insights and to build the algorithms to have the medical applications and the benefits in the medical space. Thank you for listening. Make sure you get episodes downloaded to your device automatically by liking or subscribing to the mastering medical device podcast. Also, please spread the word and tell a friend or two to listen to the mastering medical device podcast as interviews like today's and help you become a more effective medical device leader. Work hard. Be kind
