Latest / Technology Now / Why is data so important in healthcare?
Transcript
- MICHAEL BIRD Are you a puzzle person, Sam? SAM JARRELL A puzzle person? MICHAEL BIRD Someone who likes puzzles? SAM JARRELL do you count like riddles? MICHAEL BIRD I mean, like the thing with the little, like the pieces that you, you like, you know, a thousand piece puzzle or a 500 piece puzzle. SAM JARRELL I, they're okay. They're okay. I, I prefer like, puzzles in like video games or like riddles MICHAEL BIRD Yeah. Well, now I've had kids, I don't really have time for puzzles, but before kids, I loved a puzzle. Anyway. Uh, Sam, have you ever tried to complete a puzzle without the picture SAM JARRELL No, because I, I love myself and I wouldn't put myself through that. MICHAEL BIRD Yeah, they do make those puzzles where they have like a repeating picture and they're like absolutely impossible to complete. I dunno why you would do that. Anyway, in today's episode, we’re going to be discussing the data equivalent of trying to complete a puzzle without the picture, and with half of the pieces missing. I’m Michael Bird SAM JARRELL I'm Sam Jarrell And welcome to Technology Now from HPE. MICHAEL BIRD By itself, data is pretty much useless. We can collect as much as we want, but without analysing it, or examining it properly, all that collection and storage can be a huge waste of time and effort. SAM JARRELL But it’s not just the way you use the data, right? The type of data you’re collecting must also play a role too… MICHAEL BIRD Yeah, spot on. Very much so. We've discussed the saying good data in good data out quite a few times on this show, but we should probably be caveating that with incomplete data in incomplete data out, because if you're not collecting all of the data, which could influence an outcome, then you won't Really be able to predict the outcome effectively. This, this concept is particularly apparent in the world of healthcare, which is the topic of today's episode where I had the opportunity to talk to Lisa Marceau the founder and CEO of Joyuus and Alpha Millenial Health. SAM JARRELL But before we get to your chat with Lisa, I want to look at a historical example of using data processing to learn about a disease. It’s time for… Technology Then SAM JARRELL Michael, you're in the UK and so I'm sure you've heard of a little place called London, right? MICHAEL BIRD I have. I know London SAM JARRELL Perfect. I want you to picture yourself in central London Soho, to be exact. It's the 18 hundreds, so you're probably in some like old timey clothes. Maybe you've got like a top hat all around you is the scent of raw sewage and death because it is September, 1854 and in just 10 days, 500 people have already died of the third cholera epidemic. Do you have any idea where I’m going with this? MICHAEL BIRD I wonder if this is sewers. No data. Uh, um, uh, I don’t know SAM JARRELL That's okay. That's okay. Nowadays we use computers and even AI to look, for patterns and analyze disease. However, back in the 18 hundreds, one physician was relying on a much, much older bit of tech. A map. Local Dr. John Snow. Yes. John Snow really was, convinced cholera was spread through water rather than the more commonly accepted belief at the time that the disease traveled through bad air or miasma. To prove his theory, snow took a map of the area and marked every cholera death on it. And we'll put a link to that map in the show notes. When he had finished, he could see a clear essential point that the disease appeared to radiate from and at the corner with the highest casualty count. The Broad Street water pump. Armed with this map, snow convinced the local authorities to remove the handle from the pump, and in doing so with just pen and paper and a mere 500 or so data points, he halted London's third cholera outbreak. Four years later, Joseph Bazalgette would begin to build London's modern sewer system. And in 1866, a final cholera outbreak, in the areas of London, which were not connected to, the new sewers, would finally prove snow's theories correct. MICHAEL BIRD Ah, I got it semi right. I did say sewers. Um. So maybe, uh, five points. Um, anyway, the collection of health data is shown time and time again to be vital to treatment and prevention of illness. So to find out more about the importance of data in the healthcare industry and how technology is being harnessed to analyze that data. I spoke to Lisa Marceau, founder and CEO of Joyuus and Alpha Millennial Health, and she started off by telling me what Joyuus is and why she founded it. LISA MARCEAU Joyuus is a software as a service platform that is identifying risk early in postpartum space to get to care faster. And it's an evidence-based platform that's addressing mental, physical, social, and real world issues across the entire 12 months of postpartum and is identifying risk that connects to care. People often think there's a founder story, like a lot of founders. I experienced this and therefore I started this company. And while I actually did have postpartum depression, it actually isn't the reason that I started Joyuus because I didn't even know it at the time. the simple answer is really that over my career I recognized that there is a huge data gap in the way we understand health for women, MICHAEL BIRD And what's your, background like? How did you get to today? LISA MARCEAU I've spent my entire career in clinical research and a lot of it was been in conducting health, leading a health, clinical research organization, studying disease, studying risk factors, what contributes to disease. How factors beyond our own health actually genetics, biology, contribute to disease. And these patterns emerged kind of distinguishing how external factors like access or resources or how much, how much we actually know about gendered biology contributes not to the disease itself, but how diseases identified, how long it takes to identify it, how it's treated, and all of that actually contributes to what those outcomes are. Delayed identification equals delayed treatment equals worse disease, and that equals poor outcomes. MICHAEL BIRD And so what made you move from traditional research into a more sort of tech focused healthcare company? LISA MARCEAU I never really thought I was gonna start my own company, but over the journey it became really apparent that the system and the systems that are in place. Aren't serving the populations that they need to serve. And what I did see is that technology is driven a lot by kind of the emerging digital generations. And, and kind of the, the, the growth of technology in the healthcare industry was solving problems in a different way. And for me, that made it a really interesting proposition to kind of jump out of that existing comfort into something that was powerful, innovative and game changing really. MICHAEL BIRD I mean, you sort of alluded this, but like there are big gaps in women's health data, right? why is that the case? LISA MARCEAU I think the data problem and the, the gap in women's health versus men's health was established way before the 1970s. But here in the us the obvious example that is that in 1977 , the US Food and Drug Administration actually issued guidelines that effectively prohibited women from being part of research. the definition was women of childbearing age, which. Was, between like 16 and 55. So pretty much every woman was, was excluded from research. That alone, was. Something that created a huge gap in our knowledge. but there was something else that happened during this time too, The US healthcare system established in the 1940s was actually an employer-based system established to address the challenges of, people coming back from World War II and addressing wage inflation. So our employer based health model was not. Initially intended to, deliver patient care. It was actually to address, hiring practices in the 1940s post World War ii. So, understandably in the 1940s, those diagnoses and disease management practices catered to a primarily male audience. MICHAEL BIRD so fixing these gaps, I think you said before that it wouldn't just help women's health, but it'll help men's health as well. How does that work? LISA MARCEAU fundamentally right now, women's health, went from kind of being underfunded and niche to a trillion dollar market, but it's really focused on reproductive health. And, the data that I talk about now, is just math, right? Um, the reproductive years for females are 44% of our lifespan, meaning that there's 56% of our story left untold. what we wanna understand is that, for example, hormones are so much more than just a key to fertility, pregnancy, and menopause. And in fact, men have hormones and hormonal variation. And during my early research, we studied both, but at a session I attended. Back in January, A neuroscientist and immunologist, Jennifer Garrison and Eric Ulman talked at the intersection of biology, neurology, endocrinology, and immunology. And one of the points that they made, that is starting to show up in the research is that when you look at the data from say, COVID, More men suffered COVID related deaths than females during the pandemic. the research now suggests that potential biological factors like sex link variants or hormone regulated viral, receptors may have been at play, but we ignored genetics and hormonal variation and all kinds of research for decades. so it wasn't just a female health problem. it's a global problem. MICHAEL BIRD it sounds like actually aggregating data there, you can understand trends, is there like a data element here? LISA MARCEAU so when I started Joyuus, it was kind of like my goal is to solve the data gap, I was told kind of you're boiling the ocean. So we found a proof point to start in postpartum health, but when we start there, we're collecting data across mental, physical, social, and real world health across the 12 months postpartum during a period. That is probably the one of biggest stress tests on a person's health happening in a very short period of time. But all of those activities that are happening in that very. Specific 12 month period are also happening across the entire life course. So when we start to collect the data and we start to connect the dots on those data, not just the disease specific data, but we look at data across, social factors and kind of the real world contributors to disease, We're painting a much broader picture of health across the entire life course, and that's where data really becomes a resource for not just data equity, but starting to connect the dots between diseases that we aren't able to really think about or connect right now. MICHAEL BIRD and, a normal patient journey or, or just a normal journey for most people is that something goes wrong. You go and visit the doctor But that is a, that is sort of a snapshot in a particular time. you're not looking at a trend there. So are we saying that actually there is a trend. There's a moving towards actually looking at data over a period of time and, and trying to make sense of that data LISA MARCEAU I, I love that question because I think that it is the paradigm shift that's happening in healthcare for probably a number of decades. But what we're seeing now, is. Healthcare is episodic, right? You come into the doctor's office when you have a symptom and they treat the symptom in that, in that space of time where they can't see very far. Um. In the past and or into the future. Right. So what we are doing and what technology has actually enabled is we are collecting our own personalized data every single day across so many platforms. So there's an entire. health journey that we are collecting personally every single day, and we have a much better picture of our own health journeys, but it's not connected to the healthcare system. I think that is the paradigm in healthcare that's really shifting and where technology has amplified the capability to tell a much more, robust story of our healthcare than is currently available. MICHAEL BIRD can you just sort of talk me through what sort of data you collect and maybe what you sort of learned from it so far. LISA MARCEAU there's an expression. Slow is smooth, smooth is fast. and what that really means is what we did is we built slowly and methodically so that when we got to the point that we are now, we can amplify and we can scale rapidly. So we built over 200 pieces of information that really affect women and we connected. Analytics and how they use that information to see which pieces drive early disease risk. And then we did the work to create predictive modeling based on our research data to test whether we can identify risk earlier. So our data have actually shown, for example, in the national reported average, women are at risk of depression and postpartum about 12 to 15% . Our data actually show it's 44%. that's a really significant point because. what we are able to show is that there are. Women at risk of various stages of depression earlier based on all of the data and analytics that we've done and all of the information that we've created, the trust that we've built in the product so that we can actually connect them into care faster. MICHAEL BIRD and this feels like the future of healthcare, right? LISA MARCEAU think that's really important to be aware of because, depression, doesn't happen in isolation. And when you think about depression and understanding kind of risk of diabetes, for example, plus social factors that create stressors that are not captured in, an episodic. Visit, nor are like food insecurity, housing insecurity, challenges with, with other real world life factors that change during postpartum. So when you have more information, you, you can actually treat people better. I think when we actually are able to tie all these things together, we can actually provide better care and we're able to do it in kind of real time through all of the tools that we have. MICHAEL BIRD and, and what is it from a technology perspective that means that we are able to do this today what is it from a technology perspective that's changed? LISA MARCEAU What we're seeing now, and I think what COVID really amplified is that we went from a healthcare system that believed every patient needed to have a hands on. You had to see them in person. And COVID turned that. Thinking upside down. And what happened was all of the things that were telehealth that were kind of like, no, we don't need, that suddenly became the most important feature of the healthcare system during COVID These weren't new technologies. That's what's really interesting is they were technologies that had been established and were working and suddenly became amplified. Similarly, When you're collecting even like AFib data on your smartwatch, those things were kind of considered nice, but it's not really a diagnosis until COVID, and then instead of having someone come in and have to. Reaffirm that through a in-person test. They were like, well, let's go with what's data on your watch. And there were articles written about this. It's fascinating. Now, when you, make these massive changes using technology, you can't put the genie back in the bottle. Not for patients and not for providers. MICHAEL BIRD Yeah, So, I wanna talk ai, like are you integrating AI into, into your work? LISA MARCEAU I love the conversation about AI technology and innovation because. A lot of times you might hear me say, AI is infrastructure. it's not innovation and like a lot of the other things that we've talked about, AI isn't new either. AI has been around for. Decades. And what's new is that we all have our hands in it, right? and Joyuus is actually, using predictive modeling. So. there is value in ai. I think the question is not how do we use AI and what's the innovation? It's here is what we're building that is game changing and is there a role for AI to amplify it or make it more efficient and wear? We didn't start with a product that was. AI based. We started with a product that answered the questions that moms are challenged by, the providers are challenged by, and we created that SaaS platform And we got to the point where predictive modeling was really key. We can actually start to determine who is at risk without having a risk score, for example. And that helps us identify people earlier so that we can get them into care faster. Now, that's a great use of it, but it wasn't the core purpose for building Joyuus. So AI is important, it's infrastructure, and we need to start thinking about. What the purpose of the AI is. It's not just AI for AI's sake. MICHAEL BIRD so with this in mind, like should clinicians be worried about their jobs with, with tools like this? LISA MARCEAU there should never be a worry that machines are gonna take the place of providers. we don't have enough providers already and, and they're overwhelmed with kind of administrative burden and, and things like that. So, so I don't think that's the challenge. I think where we really have to focus is, What the value add is of ai, just kind of like we were talking about So the lower risk cases, the questions that can be answered with that are fairly straightforward so that the clinicians can actually focus on those last miles, the ones that are more complex and more complicated. And I think that's where we have to think about ai. where can it benefit and create efficiencies for providers so that when we have this gap and this shortage, they can be using their time, energy, and effort in ways that are the most valuable to actually improve patient care. MICHAEL BIRD So we, we talked about data. Do you think about how that data is secured? Like, is that the sort of stuff that you are, needing to think about? LISA MARCEAU we definitely think about that. making sure for example, Joyuus isn't crossing the HIPAA threshold, but we have been aware of it since the first day we started. I think some things we forget about is that it's, it's, we're actually grabbing data from individuals, so it's their data to begin with, right? And how do we make sure that we keep that trust around whose data it is in the first place. I think that's another consideration. I think when we look at this personalization of data and ownership of our own data, data security, data protections, data privacy. Those are the areas where I think there is enormous potential for companies to really, thrive in this new healthcare economy because, there needs to be a lot of work there. It, it can't be an afterthought MICHAEL BIRD Yeah. Because I mean, the economy sort of feels like, you know, the, the data economy is such a big thing, isn't it? Like going back to the top of the interview, there's nowhere near as much research in women's health compared to research and men's health, and presumably that impacts the way that some of our. Data, sort of looks like, and maybe some of the models that are maybe built on that data. LISA MARCEAU I think it's the best example of, of where lack of data, when it's inequitable really has, really creates terrible outcomes. But cardiovascular disease is the best. Way to describe this. women die more often from heart attacks than men. It's not because women have more disease, but it's because women have smaller arteries than men. And what that actually looks like is that women's symptoms are more nausea and, fatigue. men have crushing chest pain and numbness down the arm and. What it means is that women are often sent home with anxiety or kind of you're fine. And so when they go home, they are more likely to die from a heart attack because their heart attack symptoms haven't been identified. It's because women have a microvascular disease and men have a macrovascular disease. That means the symptoms, the diagnoses and the treatments all have to be different. It's almost a separate market entirely, and we've known this for two decades, yet it's still happening. MICHAEL BIRD Hmm. Gosh, that's fascinating. Lisa, thank you so much for joining us on technology now. It's been a absolutely fascinating interview. LISA MARCEAU My pleasure. Thanks for having me. I really enjoyed it. MICHAEL BIRD Well, that was a, slightly different interview to our normal interview. I'd say Sam, like the focus wasn't necessarily on the technology, but it was sort of the outcomes from the technology, whichI found that a really interesting interview to do. SAM JARRELL I found it, enlightening as a woman myself. I did not realize things like the fact that, our current sort of employer model of healthcare was based off of, Basically principles from like the 1940s,which is kind of mind blowing when you consider that it's been at least 80 plus years since then and that there are definitely some improvements to be making, but I really appreciated like when, speaking of outcomes that like Lisa framed it sort of as a data first problem, not just a healthcare problem. So, when. Women's health journeys are basically missing or a gap in the data. We can't really be surprised when we haven't built systems that don't work equally well for everyone. And what sort of struck me is that we've been making a lot of life and death decisions on incomplete data, and it's very likely women going back for, you know, nearly a hundred years now, have been paying really high prices for that. Yeah, the cardiovascular diseases that Lisa said, right just right at the end of the interview. That was, I found that so eyeopening. So, that was scary to me. MICHAEL BIRD I did like a first aid at work course where you learn about, you know, all these various things and, heart attacks was one of them and all the symptoms she said. so tingling up the arms and things like that. Um, you know, those are the symptoms that I know for heart attacks, SAM JARRELL Those are the ones I know for a heart attack. I'm always like, oh yeah, is it your left arm? All right, then you're fine. MICHAEL BIRD Exactly. Yeah. Yeah. and I think that just comes back to that data conversation. So, the fact that. there's more data from men versus women. So therefore, to your point, like the outcomes, women maybe for a hundred years have been worse. SAM JARRELL it was kind of funny to think about the conversation we had earlier about, John, snow mapping cholera cases around like a singular water pump in London. Um, and how big of a difference that made a. To me, this is just like a 21st century version of that. Like, that was like a bunch of data points on ink and paper and it changed public health. But now, Lisa is doing this, but with postpartum health and predictive modeling instead of pin and ink, different tech, but like the same principle, if we have the right data at the right time, we can make a massive difference and save lives. MICHAEL BIRD And I think in 2026, so many of us wear, devices that capture, heart rate potentially blood pressure. how much sleep you're getting, you know, some of that data can be really useful. So it feels like a, a great opportunity. Of course. And we touched on this with Lisa, like the flip side of that is. Data privacy, making sure that the data is being used in the right way and is being secured in the right way. Actually, this data is so powerful. We could do some really incredible things with it. SAM JARRELL That's true, that's true. MICHAEL BIRD now the other thing I wanted to mention is I love this line that she said. AI is infrastructure, not innovation. I thought that was a really interesting line because I think, in many organizations, there is a tendency or has been a tendency to, to say we need an ai, uh, and, but not necessarily know what we need an AI for. And I think she gave a really measured response, which was along the lines of. It doesn't actually matter if we use ai, like if that doesn't improve the outcomes then it doesn't matter if it's ai, You know, actually we want to use the technology to, do a better job of what we're currently doing. SAM JARRELL Yes. And unfortunately I think the real innovation is in what she is actually already doing, which is actually understanding women's individualized needs and looking at their unique differences. But like, AI can play a role because it is great at pattern recognition in, in seeing that full picture. And she talks about building, using the phrase slow is smooth and smooth is fast. it feels like if we can use technology to, to sort MICHAEL BIRD Give better clinical outcomes, then like, that's, that's broadly a good thing. MICHAEL BIRD Now, Lisa works at the cutting edge of healthcare research so obviously the final thing I wanted to ask her was simple: what is the future of technology in healthcare? LISA MARCEAU so I think simply put the, uh, the future of healthcare is technology and that's, um. That really kinda goes back to what we were talking about. We're tracking, we're assessing, we're analyzing, we're measuring, but it's more than just health data, right? So this is the future of health because it's combining the mental, the physical, the social and the real world factors that in combination create disease risk, create patterns of disease. Create pathways to prevention. So when data is personalized and owned by the consumer, we have a much bigger, broader picture. And to your point, when we can aggregate it, we can tell a much more complex story. So the future for technology, uh, innovation is to define, connect and protect those data. And I think that's the future of healthcare. SAM JARRELL Okay that brings us to the end of Technology Now for this week. Thank you to our guest, Lisa Marceau And of course, to our listeners. Thank you so much for joining us. MICHAEL BIRD If you’ve enjoyed this episode, please do let us know – rate and review us wherever you listen to episodes and if you want to get in contact with us, send us an email to technology now AT hpe.com, Subject line, slow is smooth and smooth as fast, and don’t forget to subscribe so you can listen first every week. Technology Now is hosted by Sam Jarrell and myself, Michael Bird This episode was produced by Harry Lampert and Izzie Clarke with production support from Alysha Kempson-Taylor, Beckie Bird, Alissa Mitry, and Janessa Ayache. Our theme music was composed by Greg Hooper. SAM JARRELL Our social editorial team is Rebecca Wissinger, Judy-Anne Goldman and Jacqueline Green and our social media designers are Alejandra Garcia, and Ambar Maldonado. MICHAEL BIRD Technology Now is a Fresh Air Production for Hewlett Packard Enterprise. (and) we’ll see you next week. Cheers! SAM JARRELL Bye y’all