Lee: Hi everyone, thanks so much for joining us today. I’m Stephanie, I’m a reporter at BuzzFeed News, where I cover science, health, and technology. And we’re here today to talk about a really interesting phenomenon that’s happening right now all around us, which is that now, unlike ever before, we can now collect all kinds of data about our bodies and our health, and our healthcare, from, like our microbiome, to our DNA, to how many steps we’re taking a day. And so it’s a really exciting time, because we can collect all this stuff in real time and try to figure out what it all means. So today we’ve got a great group of panelists, each of whom are changing the way we gather and analyze all this data.
So first, we’ve got Jennifer Tye, who is Vice President of Partnerships and Marketing at Glow. Then we have Walter De Brouwer, who is CEO of Scanadu. We have Ajay Royyuru, who oversees life science research for IBM, and then last but not least, we have Steve Alexrod, who is the CEO of G-Tech Medical.
So, I’ll start with you, Jennifer. So, Glow is working on apps or has apps that help women track their fertility and then also track their pregnancies. So how does what you do fit into the interconnectedness of it all, the Internet of bio-things?
Tye: Sure, thanks, Stephanie. So, as Stephanie said, we are a women’s health platform, and we currently have two mobile apps focused on women’s fertility or tracking your cycle, as well as tracking your pregnancy, but the goal for us, ultimately, is actually to—we are a data science company at heart, and our goal is to—as we are collecting this data from consumers and at the individual level, is really to apply data science to better understand various aspects of women’s reproductive health.
And the idea is to really identify new learnings and perhaps to confirm previous learnings about what affects one’s fertility—both positively and negatively—what affects one’s pregnancy, with the ultimate goal of figuring out how to improve outcomes, right? And outcomes may be everything from being able to conceive and avoiding high-cost procedures such as IVF or having a healthy pregnancy and avoiding high-cost, you know, sort of incidents such as premature labor and things like that.
So that’s really what we’re doing, and really—and as you said, Stephanie, we are at a point in time now where we have these computational power that we haven’t had before, where we can really apply that technology to a large amount of data—larger than anything we’ve ever seen before.
Lee: Absolutely. And Walter, so you’re working on a device that sounds kind of science-fictiony to me. Maybe you can describe it and how it fits into this wirelessness we’re all experiencing?
De Brouwer: Yeah, so, yes, we are a meta-company here in Mountain View. So basically we focus on three things: only consumer, only medical—so everything goes through FDA—and only mobile, so no desktop stuff. So we make a device, like you said, you know, we started with the idea of the Tricorder—we’re also in that, still in the Tricorder XPRIZE competition—so the Tricorder is basically—so the first part is like, you know, the first iPort [PH—0:03:35.3], is, it replaces an emergency room, but we also have disposables for like, complete urine analysis with your smartphone, and we are now starting on other markers also.
Because we want—so the mission of our company is to give seven billion people access to quality healthcare on their smartphone, so we port everything POC and IVD, you know, which now is used in hospital, to the smartphone, to a consumer who is actually no longer at home, but nomadic.
Lee: And to be clear, this is a device you hold, like, on your head for 30 seconds, and it tells you all your vital signs?
De Brouwer: Yeah, so you take it in your left—so, it’s a behavior—actually we were looking at, we were at the NASA campus, and we were looking at the way—and we tried everything, the wrist, the arm, stomach—how can we do all the vital signs in one? And then, and we needed the body to make its own circuit. And suddenly we saw these soldiers there, and we thought, “Oh, shit, that’s it.” And that’s—so we started doing—yeah, and it worked, you know, because the mathematics of, you know, need to work for the standard deviations to actually give more than just spitting out random numbers, because the government, the FDA gives a standard deviation and mean errors that we have to follow. And for that, that’s very, very difficult because it needs an enormous amount of patient data.
Lee: Absolutely. So Ajay, so you’re at IBM, and I’m curious like, what—how IBM sort of sees itself in the Internet of bio-things, and especially how it stacks up against, I don’t know, someone like Google.
Royyuru: You know, I think one important attribute of biology is that, in the last 10, 15 years, biology has steadily become an information science, genomics being just the first frontier of that, but you know, steadily we are transforming the observation aspects of biology into more and more data-driven. And omics is characteristic of that. So that actually makes the technology companies very excited about this transformation of a natural scientific discipline into an information science.
The work that we do at IBM actually goes across not just the data science aspects—in fact, I want to touch upon one, which is goes to the, you know, the microbiome point you started in your introduction. We actually have recently have started an activity that the consortium in the industry—actually goes back to the previous session where we were talking of how consortiums actually make innovation occur faster across the ecosystem. So we’re looking, actually, at the microbiome as a portal into food safety. So you know, all the way from where food is produced out in the farm, to how it is harvested, stored, transported, and packed, and put on our table and eaten—we actually think we’re eating the ingredient, right? That’s why we’re eating the food. But actually, it comes with the microbiome that surrounds it, through all those steps, you know. Even though there is a kill process which attempts to clean the food which is being put into the packaged food, there still is a microbiome around it. And because genomics has actually become so affordable and easily accessible, we can actually use the genomics as a means to tell what is the microbiome that surrounds the food ingredient as it goes through this entire supply chain, and use that as a—an extreme example would be to use the microbiome as a sentinel to tell us that something has changed in that food. You know, so there is a normal microbiome that’s usually there with the peanut as it is processed, but if you were to actually do something crazy with it, like irradiate it or contaminate it, then in addition to the food being different, the microbiome is going to respond to that. And so if you were to actually use the signature of the that accompanies normal food, can you tell that apart from irradiated or contaminated food, as an example?
So that’s one example of how data-driven science actually, I think, is going to touch upon all aspects of how—not just in the science, but also how we consume this.
Lee: And so speaking of the stomach, maybe that’s a nice segue—Steve, your company is working on a wireless patch that can actually sort of wirelessly transmit data and help clinicians diagnose gastrointestinal disorders early. So how does that work, and how does that fit into the Internet of things?
Axelrod: Right, so what we have at G-Tech, what we call an EKG for the gut, so we’re going to measure the electrical signals from your stomach, small intestine, and colon, and measure their function that way, just like an EKG measures the function of the heart. And it’s very complimentary to the data that physicians now get from fun things like colonoscopies and CT scans. And those are fantastic for discovering things like cancer and Crohn’s Disease—my daughter has Crohn’s Disease, big motivation for me to get into this space—but they don’t help the majority of people who have things like IBS. So they get the colonoscopy and they get the CT scan, and the doctor says, “Congratulations, you don’t have this, you don’t have that,” and six or twelve months later, and $5,000 or $10,000 dollars of healthcare later, you know what you don’t have, but not what you do have, and the doctor starts treating the symptoms.
So what we’re trying to do is provide some information on the function, and as you said, we have a wireless patch—here’s an early prototype—it’s Bluetooth enabled, it’s multiple electrodes. Patients will wear three of the patches on their gut for three days, and the patches will beam the raw data wirelessly to your phone. The phone will allow patients to say, “Oh, well I just had pain, and it’s in the upper left hand quadrant, and it’s a 4 out of 10. I just ate.” If they want, they can tell details about what they ate. “I just went to the bathroom,” and if they want, they can say where it is on the Bristol Stool Scale—I don’t if everyone will want to do that, but we’ll take all that data, particularly the raw data, pull out the rhythmic activity, the events, the patterns, the frequencies, the strength—put that in a report for the physician to then look at that patient’s function and say, “Okay, for your constipation or your pain or your diarrhea, this is where we think it’s coming from, and we can now target our treatment of you by beginning there.”
And for people who don’t have alarm symptoms, who don’t have a daughter with Crohn’s or a sister wit colon cancer, or who aren’t over 50 yet and have to get that colonoscopy anyway, we hope that eventually doctors will say, “Let’s do this test first, and see if we can find out a functional thing, and start treating that before putting you through the fun of a colonoscopy.”
Lee: Right, so you’re talking about taking data and then turning it into something that’s meaningful or actionable, you know, as say—
Axelrod: We’re doing some serious number-crunching, yeah.
Lee: Right. And so I think that speaks to the broader challenge of collecting all this bio-data, right? Like, we have lots and lots of it, and the challenge seems to be how to make sense of it, how to analyze it, how to make it something interesting and meaningful for patients or clinicians, or both. Like, how do you go about doing that? And this is for any one of you?
Axelrod: Well, I’ll just start. It’s the biggest challenge, right? So you come up with a device or a means of collecting data, and you see that you’ve got this signal, right? You see that you have this wonderful signal that represents the activity of the stomach or the small intestine, the colon, and you go to investor and they say, “That’s great. So what?” Right? How is this going to change what a physician does? It’s got to be something that the physician will now say, some percentage of the time, “I will now do something different than I would have done in the absence of that data.” Unless you have that, you really don’t have anything. You just have a science project or a nice publication in some journal. But to really impact healthcare, you’ve got to give the doctors something that they can understand, not the data that I as a physicist have so much fun poring over, these hundreds of millions of data points, and oh, look at that cool peak there, you know? You’ve got to give a doctor something that they can interpret and they can say, “Okay, now I know what’s going on with you, Stephanie. I know why you’re having those terrible episodes of pain every night. You know, you need to eat more pizza, is really what it is, and have more Starbucks.”
Lee: Yeah, sounds good to me. Yes.
Royyuru: I would say that, yes, you know, echoing what Steve is saying—translating the data into actionable insight is really what the outcome of all this analysis has to be. And there are two ways of thinking about it. One is that there will be some correlation, and there will not be an understanding or mechanistic-driven understanding of why a certain intervention is the right intervention. So there may not be an explanatory power to that observation of A is better than B, but it still might be the right thing to do.
That’s one way, and another is actually to have a mechanistic understanding. The power of explanatory—the explanatory power of a model can be exploited not just to intervene and act on something, but also to then go off into how to prevent or manage holistically the whole disease. Right? So actually building a model of the disease that is based on that data that then provides explanatory power is a worthy goal to have. You know, we do some of that work, for example, in modeling the human heart. We have a very sophisticated model that goes all the way from biochemical transport of sodium, potassium, calcium, in and out of the cardiac myosin [PH—0:14:04.2], to the entire human heart, where we actually would recapitulate the ECG, and that changes that occurred in that ECG when, you know, perturbations to the iron transport occur, right? So it has explanatory power, you can use that explanatory power to go and ask, what will happen if I were to put a drug on the heart? Can I tell whether this drug is going to be cardio-toxic, it’s going to actually give you, you know, visibly different EKG, before I expose the patient or the subject to that drug? So that explanatory power becomes very powerful.
Lee: Right. And what’s interesting to me is that there’s such a spectrum—something can be very much a clear medical device, but something can be more on the consumer side, too, with, like, you know the Glow apps.
Lee: So how does the role of, you know, the patient change in the Internet of things? And then, what’s the changing relationship, maybe, between patients and researchers and doctors? Because traditionally the patient goes to the doctor, and they spend 20 minutes or less, and the doctor tells them something and then the patient disappears, and maybe they follow the instructions and maybe they don’t. Maybe the doctor got it right, maybe they didn’t. So how is that changing and how are the dynamics changing between all these players?
Tye: Right. I think certainly if, you know, if I think about the work that we’re doing here at Glow, the term “patient” actually changes, right? It’s no longer patient in the classic definition, it’s really about every individual, right? And you know, what we’re doing is, we take information—and in the context of making this information actionable, that is a big challenge, and I think we’re just at the beginning of it, right? You take fitness trackers as an example, and there have been studies, many studies now done showing that majority of people who are wearing these fitness trackers are folks who are generally pretty healthy, right? And so what change of behavior are you making based on that information? Not a lot, today, right? But I think there is certainly the opportunity to do it.
One of the things that we do is that we provide very targeted information back to the individual about what’s happening to them—and I think the key is, sort of, how do you access them? You access them in way that, through channels that they’re already using, right? So certainly through your smartphone is one way to do it. And I think the other piece is delivering that information in a timely manner, right? You deliver the information at a point where it makes sense, where it’s relevant, whether it’s perhaps right before their appointment with a doctor, or right before a certain time of the month. It’s that kind of information that we do, and one of the ways in which we are doing that already is—we’re certainly not pretending to diagnose any patients or individuals, but based on someone’s cycle and data that they’re entering into the Glow log, we may be able to flag conditions such as endometriosis or polycystic ovarian syndrome, which are two of the leading causes for infertility.
We don’t share and educate every single Glow user with that information, but we do share it and target it to our users for whom we believe there are patterns that suggest that may be something they should go and talk to a physician about, and that’s exactly how we deliver that insight, is, “Go make an appointment with your healthcare professional.”
Lee: And so, Walter, if someday there is a device that could tell you all your vital signs within a minute, I mean, how does that change the relationship between a doctor and a patient?
De Brouwer: Yeah, well, we’ve done extensive research on consumers, and basically after—so, talking to 100,000 people in surveys, something very simple came out: the consumer wants to get his own data on his own devices, and he wants that data to be correct, accepted by doctors, and they want to store it into their own cloud, and once they have it, that enormous curiosity comes up for interpretation. And they go to their doctors or to their health providers, and they give that contextualized information, and they want a point of view from their doctor.
So basically they are elevating the doctor from a blue-collar worker to a white-collar worker, you know, like an information analyst. And I think it’s going to be great, because you have your baselines on your blood pressure—nobody knows his baseline on blood pressure. We all know the average human body, which doesn’t exists, but you also have your immediate binary information: I have a UTI, I should do that.
So the efficiency of the system will enormously increase, and now for the moment, you know, we—these two companies have actually continued to inspire us, that’s of course Apple and IBM, and they stand, actually, on two different sides of that game. I mean, it’s very interesting. Apple is about beauty, you know, making things beautiful so that consumers come back and put information onto it. IBM is about brain, you know, cognition, and so who will win?
Lee: So doctors are going to be data scientists in the future? Is that what you’re saying?
De Brouwer: Well, I think that there will be interpretations where the consumer will be able to choose $1.99 for a silicon-based interpretation and $40 dollars for a carbon-based interpretation, or then talk to an expert, the patient , they will have options to do that. Once they have the data, there is a big market out there for interpretation.
Lee: So we talked about patient and doctors and receiving medical care, I mentioned talking also about scientific research, and so you know, I want to talk about ResearchKit and HealthKit and what Apple’s been announcing over the past couple of weeks. For those of you who for some reason haven’t heard, ResearchKit—I’m sure this crowd knows all about it—ResearchKit is this platform that Apple’s released, an open source one, where researchers can now create apps with it for the iPhone, and so people who sign up for the apps through their iPhones can use the phones to participate in studies ranging from, like, breast cancer to asthma to cardiovascular disease, and it’s a combination of answering survey questions about your lifestyle and behavior, but also the phone is always tracking your movements, or your voice in some cases, and sending that data back to researchers. So it’s collecting all this data on you, ResearchKit is really early, and we don’t know a ton about how it works yet, or if it will work, but how do you guys think this changes the game, if it does at all, for the Internet of things?
De Brouwer: Well, I think the—so, what Apple has announced, it actually has been quite exciting for the whole industry, I find, because it’s the old theory of, you know, basically you have to find a Cartesian plane where if you have the y-axis, you have to know everything about yourself, all the omics, but if I know 100% of information about myself, I still don’t know anything, because I don’t know how I compare to all the others. I can’t—
Lee: Like, are you normal, or not? Is something off?
De Brouwer: Yeah, I cannot do predictalytics [PH—0:21:36.4], so I need the x-axis. So the y-axis is HealthKit and the x-axis is ResearchKit, and probably it will take 10 to 20 years to fill it, but it’s an enormous possibility, if you think of a complete Cartesian plane that is filled, and that you know topologically where you are in that system, it’s quite exciting.
Tye: I think it’s really interesting because, first of all, I think there’s the opportunity, the rate at which a lot of the scientific research has been done in the past, that’s already been happening. This, I think, can take it to another level. It’s also innovative in that, I mean, these are not obviously going to be clinical trials that have conducted in the traditional sense, but the power of the magnitude of the data, as well as the variety of the data that we’re going to be able to collect, I think, and leverage is really, really interesting, right? I think this notion of, not just, you know, sort of trying to integrate, I think, and I identify as many different factors that may affect—or answer, you know, whatever question it is about whatever disease that you’re studying, this helps to enable that, right?
We do that by, you know, not only is it within—even just for fertility, right? Understanding what your cycle looks like, how regular it is, that’s sort of one aspect, perhaps, to affecting, say, your ability to conceive. But exercise, BMI certainly, sleep patterns, potentially, you know, all—how many steps you take—all that stuff. I mean, I think those—certainly there are a lot of theories around it, and I mean, I think having tools and platforms like ResearchKit just kind of add to the mix there.
Lee: Yeah. I think one inevitable question when you think about the whole world being connected all the time, and you know, people being connected all the time is, can privacy—is privacy even a thing? Can privacy even exist in this kind of world? How do you guys kind of do what you’re doing while keeping in mind privacy, and is that definition shifting at all?
Axelrod: One thing to keep in mind is the value of the data. So, social security numbers are very valuable, and credit card numbers are valuable. I think the gray market for gastrointestinal motility data is a little bit lower right now.
Axelrod: But if you have a medical device, you know, you have to encode your data, and you have to be HIPAA-compliant and your databases have to be HIPAA-compliant, so you know, I think we use the tools that we have now, and then we also keep mindful of what it is that we have, and how it might be used or not.
Royyuru: I would also like to add to that, that, you know, we probably need to develop a more nuanced view of what the value of the data is, and what constitutes consent and the ability of somebody else to look at it and use it. The current situation is that we have a very binary view to that. I either consent that Steve can collect my data or I don’t consent. And if Steve collects my data, then there’s all kind of analytics he can do with it, and there’s only a matter of trust between him and me that he’s not going to use that data to reidentify or do something nasty to me, right? I mean, it’s going to—I have to go with that trust and delegate to whichever that device is, that is collecting that data, that you’re not going to abuse me. It doesn’t have to be so binary, right?
Lee: Right, well, what would it look like? That move in a more nuanced—look like?
Royyuru: Yeah, so, you know, I bring two issues here. Number one, that biological organisms are actually extremely sophisticated at presenting a different interface to different entities. You know, take a big animal, for example. Its behavior when it’s actually in the mating ritual is to differentiate itself from others like it, so it actually is trying to be very unique, and it wants to be identified. Versus the same animal when it is being preyed upon by another big animal, it actually wants to blend an look like everybody else and not stand out at all, right? And the data hasn’t changed—actually its behavior, it’s modified its behavior. The same organism is actually capable of presenting a different persona or an interface to modulate how it actually appears through these interfaces, right?
So when we digitize our life, we’ve got to have this sort of a nuanced way of manifesting a different interface for different purposes. Today we don’t have that. Today we kind of blurt out everything, you know, with the digitized information, it’s collecting all the raw bits in one interface which is an open kimono. And it doesn’t have to be that way. I think we need to build a little more sophisticated way in which we manage the content.
Axelrod: Ajay, are you saying you don’t feel that the current way that we sort of do HIPAA compliance now, where we scrub the person’s name off the data—so when we get data, the person’s name is not associated with it at all. That’s often some other—you don’t think that’s adequate? We need to move beyond that?
Royyuru: I think, actually, name is—well, it’s a known identifier, so when you know that, you know, my name is reasonably unique, so if you know my name, you know me. But actually more than that, more than my name, is the content that is inherent in me, that I can’t even change, right? My genome or my, you know, Tricorder data—
Royyuru: My phenome—I can, you know, no matter how hard I try, no matter how many courts I go to, I can’t change that, so it actually is more unique, it’s more identifiable, again, but it is so unique that when you take that information and plug it with something else that is publicly known about me, I actually become much more identifiable than my name, right?
And so that is a concern that we really have to address, and my position on how to address that concern is, it’s not all data or no data. We ought to figure out how to actually present data for the right use, so that that nuance way of presentation of the data actually safeguards the use of that information only for reasons that have consented.
Axelrod: But does that apply to Walter’s data?
De Brouwer: Yeah, I think the consumer has a different idea about this, so—because we have now thousands of devices out there, and people just, you know, they broadcast it, they broadcast their results. I think it’s true, in business you have the privacy-protection industry and the privacy-invasion industry, and sometimes they are the same people, and—you know, and that’s a bit strange. And there’s all these protections, you know, we’re talking about very big industries. A consumer, he has two—either he is healthy, and then he doesn’t care, and he broadcasts, or he’s sick, and then he doesn’t care either. I mean, he can’t shut up about it. So basically, you know, the—
Tye: I don’t think it’s quite as black and white as that, but—
De Brouwer: Well, you know, of course, I’m now simplifying it—but you know, a consumer also, you know, we are no longer the uneducated, unwashed majority of the 19th Century. So we go to Walgreen’s and when I go for Lipitor and beta-blocker and Zantac and the insurance company knows exactly what I have.
De Brouwer: They can—and the doctor cannot say, oh, I’m bound by secrecy—the doctor cannot say, “There is this Person X, and he needs that medicine, and you have to pay it back.” No, they want everything documented. So for an insurance company, you don’t have to hide it.
Tye: But I think the theme, actually, across everything that has been discussed is, it still assumes this notion of treating, sort of the individual data with some level of respect, right? Which assumes some way of, sort of this notion of privacy. I don’t think that goes away, I really don’t. But on the other hand, there is absolutely opportunity—and we’re missing, I think, a lot of value, by not looking at the individual-level data as it maps to, say, an anonymized crowd’s data, to look for, you know, okay, what are the unique identifiers for each individual? What could we—what do we know about this person that we can customize exactly how, you know, what their actions should be or a recommendation to them would be to improve their outcomes, right? There’s absolutely value there and so to the extent that individuals, or consumers, or patients provide consent to provide that information, I think that’s usually valuable. I think it still assumes some level of consent, right, but as long as we can do it in a way that respects their privacy—which I think is absolutely paramount—we have to do it.
Lee: Yes, it’s really interesting to hear the different viewpoints and clearly it’s a topic that continues to evolve as technologies evolve, right? And so while we’re kind of looking ahead to the future, I mean, we’re collecting all this data—what do you think will be, kind of, the endgame? Like, a couple years out, when we’ve got troves and troves of it, like, what do you hope or think, what will come of this mass accumulation? Any of you.
De Brouwer: Well, I think you’ll see that our body has a homeostasis—so when we are sick, the body tries to balance itself. We try to go to doctors who find out how we can speed up the homeostasis, but just suppose that—and actually it’s Eric Topol in his new book came up with that idea of the extrinsic homeostatic device. So if I have a device and I look at it and I see what’s going on in my body while I’m looking at it, actually I’m creating a feedback network with my brain—I want these numbers to be different, and I’m going to take probably 100 decisions—you know, like, okay, I’ll walk the stairs, or I don’t eat so much—which I don’t even remember. And that’s actually the very, very small decisions that make change in our behavior.
Axelrod: We’re really excited about the potential after we’ve got a million tests out there. At the individual level, we think we can help people, by doctors looking at their data and how they differ from average. But once we’ve got a million or a few millions tests out there across the country, you can start looking at the patterns when people live in Seattle and they drink a lot of coffee, what are their guts doing? People live in Alabama or Georgia and they have all that wonderful fried chicken, how do their guts behave. And it’s the same that others have been saying here: once we see a lot of data and we see what the typical patterns are and the range on those typical patterns, there’s so much that we can learn, and this is one of the big things that are coming now from this explosion in information. Like, that we will know so much more about where we are in the context of the rest of the population and I think that at a high level, university researchers are going to have field days with these things, being able to see what those patterns and what we can do to take advantage of what we now know. I think it’s very, very exciting.
Tye: Perhaps this is oversimplifying it, but I guess I’d like to, as I think out to the future, I’d like to—I think what we can do with all of this data is to better predict outcomes at the individual level, and hopefully, actually better figure out how to intervene to perhaps reduce the incidence of those more negative outcomes. Whether it’s better at, you know, identifying right up front which individual has, you know, sort of this aspect of say, IBS or IVD, right?
Axelrod: If we can get people to feed back and say, “This is my situation, and this is what worked for me, and this is what didn’t work for me,” then we have tremendous opportunities.
Tye: Or for us, for Glow, better predicting which 25-year-old is going to have trouble conceiving naturally, or which 39-year-old—and sort of the whole range. I think if we can do that up front—before, ultimately, before they even tell us what’s worked for them or not, right—if we can better predict that, I think that will be hugely powerful.
Lee: Absolutely. Well, great. This has been an awesome discussion. I do want to leave a few minutes for questions from the audience, so yeah—stand on up.
Audience1: My name is Auda [PH—0:34:26.0], I’m a nurse working at UCSF, work as a site supervisor for the women’s clinic, so I’m delighted to be—I originally brought my son, so I’m going to embarrass him a little bit, just to expose you to technology in medicine. But I’m delighted to be here because I feel this is just bringing the patient to an equal level of understanding. In regards to their own health, I feel like the past years’ generation has been all about disease management and now is more about health management.
So when the app about—you know, I deal a lot with women, so a lot of women have issues with fertility and people also that have a lot of issues with gastric symptoms, so we’re trying to figure that out. And it could be very complex, it could be very simple. So I do appreciate the fact that there apps that can give that size and symptoms and bring that data to the office, because it makes the office visit a lot shorter and a lot more focused on what the possible intervention could be, and it shortens the amount of treatment time, and it is cheaper for the patient. As a consumer, I like what Jennifer Tye said—we don’t want to see the patient as a patient anymore, we want to see it as an equal individual, as a consumer, and I think the more prepared you are as a consumer, when you go to the doctor, the better you are. I working in the past with patients with chronic conditions, so my job was simply to teach them a lot about the symptoms and the disease process, so when they went to the doctor they have some good questions, or more questions, or more concrete ways to seek what the doctor will say.
So my question, actually, is how do you feel, what are you doing, like, for instance with the blood pressure? Or with the patch? When they come to the clinic, we do some things to evaluate the quality—QA, you know, we do QA stuff for the pregnancy test. Before we introduce the test, we do certain things to make sure that the actual test kit is working. But when you do it at home, what are the risks and what are the companies, and the manufacturers like yourself or innovators like yourself, doing to ensure that it’s less errors and therefore less concerns coming from the medical providers, but also the consumer/patient?
Lee: That’s a great question. Does your stuff work? Is it accurate? Yeah, how do you know it works? Any of you, I guess.
De Brouwer: So, well, basically if the consumer—and I know that there is, there are several belief systems at work. There are, of course, more conservative powers that believe that consumers are not yet ready to take their own measurements, and that they, in some way, because of their unanointed status, could be contaminated—but this is the minority. And I think the FDA, the regulator, has put it actually simply by saying that the consumer—everything OTC has to have the same standard deviations as hospital, as clinic, as medical, as doctors, and you just have to abide by it. It is just very difficult, because the problem too that we have in the medical industry, we have to actually make stuff next to the giants, like Phillips and Siemens, and for that it takes thousands and thousands of patients and data points. We pay $500 for one data point—so if I need to calibrate my device—and not fully cost-loaded—so if I need to calibrate a device, and I need 1,000 patients, and I need their blood pressure, I have to pay $800 to $1,000—because it’s two data points, systolic and diastolic—so that’s actually what creeps into making it accurate. And I agree fully that—so consumers should be able to take their own measurements, but they should also be entitled to have accurate measurements. That’s why, you know, FDA is there to peer-review the sort of leveling the plane between the doctor and the consumer, and so for instance, we have a urine analysis panel for—
Lee: I’m sorry, Walter, we only have like five minutes left, so let me just do some more questions. I’m sorry, go ahead.
Audience2: Terry Synowski [PH—0:39:50.2]. My wife is a physician at the VA in La Jolla, and what she tells me is that she’s spending less and less time talking and meeting with patients and more and more time sitting in front of the computer filling out forms, dealing with all kinds of regulations, and it seems to me that what you guys are talking about is going to make her job even more difficult. And the reality is that it’s very low-tech—it seems like it’s a solvable problem, and it seems like low-hanging fruit, and is there anyone working on that?
Tye: That is a whole industry in and of itself, to solve sort of the problem that your wife, I think is dealing with. I think, I mean, you said, well, is what we’re doing making her job ultimately even more difficult. I think fact of the matter is we have to be able to educate and empower individuals with information about their health, right? The capabilities are there, it’s the right thing to do, and if we can do it in a way that facilitates actually, as we said earlier, more efficient and focused conversations with their healthcare providers, that’s actually a great thing, right? Instead of coming in and doing a slew of tests for, you know, “Not sure what’s going on, but I’m not comfortable, you know, my GI system isn’t quite right,” and then coming back in for more tests and more tests, only to figure out, well, you don’t have cancer—you know, can we actually have a much more focused conversation up front, with, “Well, here’s what I know so far, right—here’s what my results have said so far, or here’s what my, you know, the Glow app has told me about my cycle and whatnot,” then start the conversation there, I have to think that will make things easier.
Audience2: So, here’s what my wife tells me—that patients already are coming to her with information from the Internet, and having their own self-diagnosis, and they want these tests done, and basically she’s now a servant, basically to them, instead of the other way around. In other words, I’m not sure that what you’re doing is really reflecting the reality on the ground, in terms of what the doctors have to—
De Brouwer: Well, I think the reality on the ground is very different if you talk to consumers. And I spend a lot of time in hospitals and with patients, and basically everyone is unhappy; it’s not only your wife. It’s also the nurse, it’s the administrator, it’s the patient. Everyone in that hospital is unhappy, even the people visiting it. So it’s time that something changes. As consumers, we want to take a part of that work on ourselves. And that of course, assumes that we also take responsibility for it, so it should become a lot better and more efficient. So.
Lee: Yeah. One more question? Or two more?
Audience3: Hi, Alison Sullivan from the communications firm W2O Group [PH—0:42:38.4]. This is kind of a related question, which is how do we equip healthcare professionals to make sense of all this novel technologies? There’s so many apps out there, there’s so many patches and ways that they can collect data—how can we make sure that they using them intelligently and providing insights?
Lee: Great question.
Axelrod: I’ll take a stab at that. I think it’s very much the burden on us as a provider of this information and take this hundreds of millions of data points and reduce them to something that the physician can efficiently interpret. We will not do the diagnosis—we are not qualified, the physicians are—but a big part of our challenge is to do exactly what you’re suggesting, and that is to put it in terms that they can understand and put to effective use quickly. So to get back to the previous question, we’re not trying to make life more difficult, we’re trying to make life easier, and it’s the electronic health record, the electronic medical record people that you maybe want to go yell at, and not us. We’re the guys with the white hats here. But very much the burden is on us to come up with ways to present that data, and we have to work with physicians in the development phase to make sure that we’re doing that.
De Brouwer: And part of that work that is now put on nurses and doctors is because hospitals silo themselves. It’s easier to get missile launch coordinates out of North Korea than to get your CT from a hospital.
Axelrod: Did you do that, Walter?
Tye: Yeah, he speaks from personal experience.
Lee: Maybe one more question? No more questions. Okay. Well, thanks so much to the panel, thanks to you guys for being a great audience. Thanks for coming.