DataFramed
DataFramed

Episode · 7 months ago

#90 How Data Science is Transforming the Healthcare Industry

ABOUT THIS EPISODE

The healthcare industry presents a set of unique challenges for data science, including how to manage and work with sensitive patient information and accounting for the real-world impact of AI and machine learning on patient care and experience.

Curren Katz, Senior Director for Data Science & Project Management at Johnson & Johnson, believes that despite challenges like these, there are massive opportunities for data science and machine learning to increase care quality, drive business objectives, diagnose diseases earlier, and ultimately save countless lives around the world.

Curren has over 10 years of leadership experience across both the US and Europe and has led more than 20 successful data science product launches in the payer, provider, and pharmaceutical spaces. She also brings her background as a cognitive neuroscientist to data science, with research in neural networks, connectivity analysis, and more.

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You're listening to data framed, the podcast by data camp. In this show you'll hear all the latest trends and insights and data science. Whether you're just getting started in your data career or you're a data leader looking to scale data driven decisions in your organization, join us for indepth discussions with the data and analytics leaders at the forefront of the data revolution. Let's dive right in. Hello everyone, this is a dull data science educator and the vengelist at data camp. Two years into the pandemic, the potential for data science and machine learning and healthcare has never been more apparent. Whether it's drug discovery, acceleration, operational innovation, virtual assistance and disease prevention, the margin of opportunity for data science and healthcare is massive. However, that doesn't come without its own set of unique challenges and risks that require unique solutions. This is why I'm excited to have current cats on today's episode of data framed. Current is a senior director for data science portfolio management at Johnson and Johnson. She has decades of experience at the intersection of healthcare and data science and is deeply attuned to the state of data science and healthcare today. Throughout our conversation we discuss where the landscape of data science and healthcare is today, the unique challenges of applying data science and healthcare, the importance of ethical ai when working on healthcare use cases, is how to solve some of the data challenges of the healthcare industry use cases. She's been excited about how data science was used backle covid nineteen and much more. If you enjoyed this podcast, make sure to read us and subscribe and at a comment, but only if you enjoyed it. Now let's dive right in current its great to have you on the show. Yeah, great to be here. Thank you for having me. I'm excited to talk to you about data science and machine learning and healthcare. You're experience leading data teams and complex organizations and how you've led rd Johnson and Johnson. But before I'd love to learn more about your background and what got you into the data space. Yeah, absolutely so. I guess like most people, I've always loved data and my first statistics courses I started to think, oh, this could be really, really fun, and especially when I started applying it to data I had collected as a research assistant. It was pretty addictive. And then as I moved along my career. I'm a cognitive neuroscientist by training, but did FMRIRI research as well as looking at some like large epidemiology data sets, and twenty years ago wrote a paper and predictors of suicide attempts. Not exactly an AIML approach to it, but that interest in know like how can we predict some event? And then I had been a neuroscience studying neural networks, all of these things and applying actually machine learning techniques to fmri images, which are images. Well, someone's doing something. So it's a fairly complex as all the old clean data set got me really excited. And then...

I've always been passionate about healthcare and solving problems and healthcare. And my first corporate data science job was at highmark health. So I started on the payer side, building a bunch of models and seeing how those models impacted care and was hooked and then moved to the parent company. It's an integrated healthcare system, second largest integrated pair provider system in the US, and started a data science department at that parent company looking at the payer, the insurance side, the provider side and a few other diverse about healthcare businesses and then came to Johnson and Johnson where I am now, and it's been a really exciting career where I get to see a lot of impact from data science. To start off our conversation, I'd love to understand the current state of data science and machine learning and healthcare. Early in my career, about five years ago, and that's not too long ago, and healthcare was often and still is, talked about as an industry with a large margin of opportunity for data science, but it comes with its own unique sets of challenges, which makes it slower and comparison to other industries. Given your experience as a data leader and healthcare, I'd love to first start off our conversation by understanding how you would describe with the current landscape of data science and healthcare looks like today and how is it evolved in the past few years? Oh yeah, that's an exciting question and it's it has evolved and different parts of healthcare, I'll say, or probably at different places and evolving and at different paces out of sometimes necessity. And you say there's a lot of opportunity and healthcare. There is, and I think it's one of those industries where you have to take a bit of a careful approach to anything new. They're practically their regulations and there's a lot of risk for something going wrong, but huge benefits. But what I've seen over the last few years is really a couple things that we're seeing in a lot of industries, but in healthcare as well. Scale, as we're moving into hey, the data science can be very, very useful for solving real problems, and healthcare there's a focus on deploying these models and not just having proof of comps concepts, but really using them to drive core business decisions and core insights, and and that requires data science and scale, where at first it was a little more experimental, a little more well, let's just see how this goes alongside what we do today, but we're not going to go all in and really use this to drive our business. But we're moving towards that. The other change, I guess, are the problems that that we can solve, or just were realizing them right. We're expanding the scope of what data science can do in healthcare and of course there's diagnostics, there's also operations, there's clinical trials and how those are on how patients are found. There's so many things we can do. And then a third I really important.

I wouldn't say change, but something that's just continues to mature and we think about and I think it's helped accelerate data science and healthcare. It's just thinking about the ethics of what we're doing, considering its impacting people and the care they received, and can be life or death, or it can either help or hurt disparities we're seeing in care. So really have thinking about ethics, which is important, and healthcare and then having tools and ways to address that at scale has really evolved over the past few years. That's really great. Then I'm excited to impact these with you even more so. You mentioned at the beginning some of the areas of impact that data science and machine learning have in healthcare. Do you mind expanding on these main areas of value where you've seen data science and machine learning push the envelope forward within the healthcare space? It's hard to pick a few, but one I love to talk about in this is something my former team did and I really I love the way they approached this and I saw it impact patients. Was Looking at operations. So sometimes in healthcare we go at the we're going to cure this disease, we're going to diagnose this disease and of course, how do we not say we're going to put every data science tool we have towards cancer? And we should, but a safer way in and a way in that makes a huge impact can be the operations of healthcare itself or the operations of a clinical trial. So I'll give you an example. When I was at him our health we built a tool to help schedule patients receiving chemotherapy, and a big thing for me to start with, the problem we heard about. Hey, we're scheduling in patients for chemotherapy. They have long wait times, which seemed not great. We notice we're really busy in the mornings and then things are empty in that afternoon. So are clinicians are either overwhelmed or don't have a lot of patients. And we dug in. That was two things. They didn't know how long a treatment could take and there could be side effects, and clinicians want to care for their patients and make sure they have plenty of time, so they're blind to how long each patient might need staying there in that location. So if we're able to predict that weekend, start efficiently scheduling and then just optimizing the scheduling, optimizing the operations. Where in the calendar can this go? Where location wise can this go? And we had this tool ready when the pandemic started and it became even more important to space vulnerable patients out. Started with an operational challenge, though. Scheduling very practical thing to solve and it made a huge difference. I I've heard and stories from patients and saying Hey, I can get on in back to my life and not wait, I can come at times convenient to me. But now their area that I've seen an impact in. A lot of promises diagnosis or detection. Early diagnosis, early detection to give clinicians some some time to intervene. We heard about this and things like sepsis or acute diseases, or talking about early...

...detection of things like pulmonary hyppertension, which is frequently diagnosed late, and I know that something where we are doing now. These are big, big areas of opportunity where we can treat patients because we can detect these diseases and diagnose them and then the Thirda is patients own experience with the operational opponent. Of course, that had a patient experience piece, but I just understanding a patients their journeys, where they're facing challenges, how they're experiencing the healthcare system and where we're not maybe delivering care in the way we should. Data can help us see that and help us deliver a better experience, deliver a more personalized, tailored experience on a biological level as well as just an individual level, preferences, ways of interacting and ways of receiving care. I love how you framed the operations component here, because whenever we talk about data science and machine learning and healthcare, we always talk about aspirational use cases that I think we're all in agreement or extremely important. For example, I'm very excited to see the impacts of deep minds, alf of fold and direct discovery. But that doesn't mean we cannot create impact on people's lives right now with data science just by solving operational challenges. When talking about data science and healthcare, we often talk about challenges unique to the healthcare space, such as access to relevant, interoperable data, ethics of AI and a host of other challenges I'd love it if you can break down what are the main data challenges you think that a healthcare industry is facing today. I talked to my colleagues across industries. Everything man actually not a motive, just very different industries and no one tells me our data is perfect clean. Haven't really had a problem there. Thought about it. Of course you're not surprised to hear this. And then help care. We face that as well, and interoperability and different formounts of data. We're facing the same things. But I think we're realizing that a other industries that face this and be you know, there are solutions that will work here as well. It's the whole topic the ethics of Ai is is huge, a huge one here and really, really important. So this becomes crucial and healthcare. I'm not saying if you're selling a consumer good, of course you don't want to make a mistake, but if I get a recommendation to buy a toaster of an and I just bought a toaster of and so I'm probably not going to buy a second one in this just happened to me. It's not a big deal. It didn't really affect Fri you can experiment with those algorithms, get them out there and get them out there quickly. And in healthcare we've obviously had to think and other industries face this as well. There's risk, so you have to really think through what you're doing and what could happen and how this algorithm is going to work. What how you're going to build this process and get it right. That's not to say there aren't things we can do. There's a lot, because there are a lot of problems and things we're not doing really well today. So, as long as we're...

...not making it worse, we should try some things, but that's always going to be a pretty big challenge and an important challenge that we should take on relative to other industries. Let's just talking about the data. Obviously the sensitivity of the data itself makes it maybe a little harder to get access to data or think about how to use it, share it, what kinds of environments that data can be in and it should be. I mean, that's a challenge we should take on as a good challenge and the one we say we were never good enough, because this is the most sensitive data and people's lives. So we should be continuously improving and thinking about how we protect this data, how we use it, how we make sure we're using it in a way that decreases inequalities and how we deliver care, which I think it can. But we have to use the data responsibly and consider it is very, very sensitive data, maybe more so than if there's a leak of that. I bought it toast up and not that excited, but I buy comic bigger now. That not that exciting, but this, this is a pretty big one. I completely agree. Here in the spark the chat a bit and talk about the ethics of AI and healthcare. When we talk about using machine learning and a in healthcare, there's this version that whatever we develop will end up creating harmful outcomes or that it could be used irresponsibly, and oftentimes the response is not to leverage machine learning in AI. So I'd love to understand how you evaluate the risk of harmful outcomes of machine learning in the Eyron healthcare and how do you go about minimizing it. Well, a great question. One big thing to understand the potential harmful outcomes. You have to understand the problem that you're solving. Be Working collaboratively with a cross functional team with clinicians, with whoever is using and implementing and acting on your model, with patients. You have to have everyone in the room and involved in this process and understand that end end, because that's the only way you're going to find where the risks might lie. You have to understand how how they're going to use this information and make a decision. What mitigations can you build and where the risks at every point in this system, and that is sometimes something data scientists, especially when they get started. They're excited to build models and they skip over this piece of it unintentionally. And when I read about, you know, Resumese I from the HR world, like the algorithms going to learn what you feed it, and historically data reflects our human biases. So the Algorithm, if you don't think about it and you don't account for that, is going to learn to do exactly what people have done, which is not really necessarily ethical. But when with data and with an algorithm, we have an ability to fix that into control that a bit more than than we do in people. But I always think about the end end, how the decisions being made. It can't just be about the algorithm. And another part is it...

...sounds kind of simple, but empathy in the human center to design thinking approach is very valuable for data science because you start putting yourself in the shoes of the the person who's affected by this, the patient, all of the things they may be facing and all of the things that may happen based on the Algorithm. So you've got to really think about it from that angle. And then it's of course, the technology, the data itself. What biases are there, the algorithms you're choosing, the ways you can mitigate and correct it? Can You? And that's job a technical expertise, a data scientist, has to have and it's the essential now, especially in healthcare but everywhere. We want to think about that. The other obvious one is really going way back and saying did we pick the right use case? And like the operations example, there's a lot of problems to solve in health care. We should be thinking about all of them, but maybe the easier quick wins are ones where there's a little less opportunity for harm. If it's maybe we're just randomly. We're communicating with everyone in the same way today, and maybe if we try to figure out some preferences and try to customize a bed and learn from there. That may be lower risk than detecting a disease or changing the course of care. And in medicine and health care this doesn't replace a clinician. We want this to enhance a clinicians decisionmaking. That's awesome and I love how you draw inspiration from other fields like human centered design. Given that, do you think also healthcare can draw from risk management to risk analysis to create a governance frameworks? I think that is a great question and absolutely there is no industry we can't learn from. We have to be looking outside of healthcare all the time and looking across healthcare to different parts of healthcare, but definitely looking outside. That's why I very intentionally hired people from other industries on my team's I've wanted people from manufacturing and and it has work. They've come in and looked at things and said this is not an easy but a pretty easy problem to solve. We deal with this all the time and something that somewhat my background is mainly in healthcare. I would think scheduling, certainly movement of chemotherapy drugs around a different locations. That I thought as though that's a pretty big challenge, but I knew that other industries have solved it and so I look to people from those industries to come in and bring some of that thinking to healthcare. Risk Management, of course, that is something we do. We have risk mitigation plans for everything. We do think through everything early the the every industry we need to be looking outside all the time in healthcare when thinking about some of the other obstacles that are unique to healthcare, such as day two access, intrall perability and collection when used to change so that data science healthcare innovation accelerates here, is it regulatory innovation industry standards that need to evolve?...

The regulatory component is there. It's important. There's collaborative work and discussions going on across healthcare to make sure the the regulatory environment needs the needs of data science. That's an ongoing process. Another one, though, that maybe is every industry, but I see it a lot in healthcare this systems are very complex. We have different EMR systems. Those have a lot of steps in pieces. Data scientists don't always understand how a clinician interacts with that system. Yet that's that may be the place where their solution is delivered and acted on where the value is realized. But they're very complicated system and to get them all to connect. Maybe we want to use multimodal data from multiple sources, imaging devices, everything, to really get a full picture of the patient at different time scales. To really scale that solution and implement it, we need those systems connected. You can do at once, grab all the day to put it together, build a model, but how do you then deploy that model? Seeing some simplification of these systems and some consideration the hey. It's very important to use this data to deploy solutions and to seamlessly connect and simplify things would be great to see and I think we're probably going to see that and I as I said, it probably exists in other industries as well. The other one is experience with data science, data literacy, see or AI literacy. We don't need clinicians and hospital administers. They don't need to be experts in data science, but I think is we all bring up that level of understanding and understanding how data science works, how some of the stuff can be used and be able to speak a bit of the same language. That would help, and we're saying that in again in every industry about one I think we have a good chance of solving. And in medicine a lot of people on a scientific background and it's a data science has the science, so it should be a good place and I've seen a lot of engaged clinicians and a lot coming with a lot of knowledge experimental design and that's moving along. But we could be better there and we need to keep pushing. And that data literacy component is huge from a data quality perspective because a lot of healthcare professionals are the ones who are in putting this data into these systems and if they do not recognize the role the data plays and the value chain of data science and that value chain will end up breaking because no one is paying attention to the data quality. Right. That's a great point and it actually that data literacy. Then it's going both ways. It's a business literacy on the data scientist part of understanding how a clinician is in putting data and how they're interacting with an EMR system or how on, you know, the insurance side, maybe a care manager is identifying and reaching out to them vers of...

...an insurance plan to help them ordinate their care and manage a chronic disease, but we have to understand how that data comes in. And, conversely, if we show the value of data science, the people delivering care and part of that healthcare ecosystem are going to be able to work with us and say, okay, like I can, I can see the value of this distinction, as long as we don't take time away from there in our actions with patients and make it harder. Don't want to do that. That's awesome and, given we're discussing the value of data science and healthcare, I'd like the pivot to discuss your experience as a data and AI leader Johnson and Johnson. I'd love to understand and think through some of the most exciting use cases you've seen data teams working on, especially in healthcare of Johnson and Johnson, especially given what must have been a very interesting time for our in D teams with the release of D Jane, Jacovid Nineteen vaccine. Yeah, there are. There are three that really come to mine and one we all are so deep in it. It's always a great example. So this is this is something I think is an excellent example of using data science to solve a real problem and make an impact when clinical trials are planned, as you can imagine, their complex. There's a lot of planning and you need to decide where to have those trials. In the case of the vaccine, we needed to find places where covid was spreading so that we could see whether this worked quickly and get it out to people. And what the teams are able to do using data science was predict where these future hot spots would be in plan the clinical trials in those places. Then it was effective and it allowed us to accelerate that and be really targeted and where we are doing clinical trials and where we're seeing high levels of covid so I think that's just a very great example and it shows data science can rise to the challenge and really solve big problems under pressure when it counts. With there is no bigger really pressure and recent times than the whole world's in this pandemic and we need to do something about data science. I'm really proud of that. The other I think I mentioned the learning your hypertension example, but just one example of how we can bring data together and use AI to diagnosic condition earlier and that and that's something we're doing and working on that's very, very exciting. This is an under diagnosed disease or it's not diagnosed early, when when we could treat it and make an impact. So if we can bring together diverse data sources and predict that diagnosis, we can really make a difference in people's lives. And then the third is just generally using data to accelerate what we're doing and how we're doing it at every part of the process. We could talk about that all day, but using digital data and digital and points to better majure outcomes, using real world data. Claims Day Eh are data to really make sure we understand the patients, we understand their needs. Were developing drugs that are going to make a difference in we're doing it efficiently and quickly, because it always strikes me...

...that every day that this is not out there, patients not getting this treatment. So I love that we are always focused on how do we get medicines to patients faster, because this matters and we all either have that, know someone or will be affected by this. I absolutely love the COVID nineteen use case here and it's really exemplary of a data science use case that requires relatively simple data science that can provide value now for patients and healthcare providers. So I'd love it if you can impact that use case even more and maybe discuss the methodology used here. I think it's a general process that really is important for solving any data science problem and at a high level, and I've done this set up very multiple companies, it starts with identifying a clear problem, in this case right it was. Clearly we don't know where to plan to have these clinical trials and it's not something we can spin up in a day. It takes some time. So how could we know earlier? It's finding that problem that can be solved with data science. That's one piece that was crucial here, and then it's collaborating, working together with the business clinical areas to design and implement that solution in time. Sometimes data scientific gets to exploratory or just experimental. We're not thinking about the urgency in the timelines where we need to deliver and working closely as a core member across the team and to to make something like this happen, you have to do that. Those are just two key things that have to happen in any high impact data science use case, and I think ones that have served well. And then the third piece of advice I got very early and I've always used and I've seen as a component of successful projects is really understanding how the solution your building is going to be used and making sure the people who are going to use it are involved in the planning and have bought into this, because you if you don't have adoption, you're not going to solve the problem that that you wanted to solve. So I think one thing that's evident is that there's a lot of different data teams at Jane Jay Doing Different Work. It's one challenge to do this data science and healthcare, but it's another challenge to work in a large matrix organizations where there are tons of stakeholders and a lot of different teams working on different problems. I'd love to know how you ensure that you're staying effective despite this complexity, and some of the best practices you can share in managing and working with data teams in large matrix organizations with other data leaders. I think a big one is coming back to the shared mission vision what you're trying to do, because in a healthcare organization, or any organization, but definitely in healthcare and at Johnson and Johnson it is very clear we are getting medicines to patients, were saving people's lives at the end of the day. A...

...that cuts the matrix, the complexity of a large company. Sure it's there, but the culture in the focus on the patient and what we're doing unifies and brings us all together and breaks down those silos. And I think if at any company, if you find and focus on that the problem and what you all care about, how everyone's benefiting, it really helps the other is something I think it's just crucial bring people in early from across your company. It becomes more complex when data science happens in the silo and then you show up with the solution and different parts of the business or think you know, no, we needed to be involved earlier, or this is slightly off here and it can be harder than it needs to be, which is brings me to the good part of a mate large Matrix Organization and why I keep working for them and I love to be at one. I love to be the leader in a large make strin organization. You have incredible resources, you have experts, you legal teams, you have supply chain, there's there's so many experts in the area where you're developing solutions. That's it is a luxury to have when you're a smart of I talked to companies people that have great ideas and they have to work so hard to just get access to Hey, can you just tell me about some of the problems you have or how this works, and they don't have all of these resources surrounding them. At a large company, you have so much support and you can never reach out too much or too early and think about, Hey, you know what I'm struggling a bit with. Maybe how do you think about marketing? Oh, we have a marketing team. They everybody loves to get involved and they love to help, and most companies, I think you'll find this. So reach out and use those resources that make a large company great, because otherwise you're going to have all the bad parts of a big company and not have a good parts. And that why do that? That's great that. It must be especially rewarding to have access to healthcare subject matter experts across the value chain, because this will help you develop this empathy to create human center, Data Science Solutions. Exactly. No, absolutely, and we have that easily, just phone call or quick message away, like we are. People are happy to talk and using that is key. Yes, it's wonderful to happen, great to use, awesome. So I'm sure these conversations with subject matter experts also influence your road map. Given the importance of R and D in the healthcare space, how do you ensure an adequate split between long term research and short term wins that can help you move the needle? Yep, absolutely, and right now and then this R and d environment developing medicines and it's a long term view which is really interesting to see and to have that said, there's a lot of short pieces and wins along the way to get to that end goal. So if you working with the clinical teams, and as we do,...

...we really work together, or in any company you're working with the business area and talking about what is that end to end, what's the ultimately how long term outcomes? And then work backwards. What are the short pieces and those quick wins, as you say, a lot to get you there. You get that mix. And then I think it's important to look at at the portfolio you have for data science and go through and see how many of these are really it's going to be years before we see the value, and that's something in data science you need to know because you have to be careful not to let that timeline and the pace of technology and changes conflicts. You've got to think about it early. But yeah, looking at how many long term projects Y have? How many short quick wins do I have? And then also it's okay to have purely exploratory I'm going to play around with this data see if I can develop this model. That's great to have to it's just looking across the portfolio and making sure that the percentage of work that's in all of these buckets is where you want it to be, in needed to be. And how do you determine which areas research in your rd agenda? The good thing is in an rd organization that happens at such a high, high level. But to bring it back to one simple concept, it's unmet need and what the patients need, and I think it's something that applies everywhere. That where's there an unmet need where we can bring data science and but of course that's goes into the planning of what do we develop? And it's a pharmaceutical urd organization. It's a big process. It's the core of the business and then there's the data science component. How does data science support and accelerate and enhance that that portfolio and that that R and d process and as we mature and talk to each other and data science grows, which we're doing a Johnson Johnson Jansen R and D, which pharmacutical companies of Johnson and Johnson, the data science team and capabilities are just exceptional. Shot Com is our chief data science officer, has built just a really incredibly advanced capability and and the company is putting a lot of investment into data science in R and D and commercial and across the company's great to see and that shows me that there's it right. We've had the discussion about this can impact the rd portfolio, this can help you meet your goals and we've had that conversation. Has Been Successful and that's why we're able to grow and really use data science now current as we close out, I'd love to have a look into the future and what you think are the data trends and innovations that you're particularly looking forward to see within healthcare. One that is very important and I'm very excited about is the concept of fairness. So we talked about the risks and reasons people don't want...

...to use AI and healthcare and and this one comes up a lot and it really any kind of high stakes industry. It affects that industry. But I'm really excited about the capabilities in the thinking that that's evolving around fairness, both being able to detect bias and unfair pieces of the Algorithm and then even fix that on the fly at scale make corrections. I think that has the ability to allow us to really use data science, AI and machine learning and healthcare. But it really brings a ton of value to people, to patients and make sure they're getting care that is fair, that we're considering things that maybe we haven't been great at in the past and maybe this can make medicine a bit better or any field a bit better. So fairness is a huge one for me. Future trends, of course, I think we're going to continue to see scale. We're going to continue to see a bit of a I don't want to say a catchup, but we're in a nice position to lead from other industries that have really perfected or made a huge a lot of the advancement and embedding ai into every part of their business. We can take the technical learnings and platforms and pieces and start from there and healthcare, and I think we're going to see that continue to grow because as we start making an impact, we're going to need to consider how this becomes a core part of healthcare. Because, ar in, it was great to have you on the show. Do you have any final call to action before we wrap up? You know, it is to focus on the impact. Like I just always encourage data science and data science leaders to think through how is this stata science solution solving a business problem? How is it making an impact and how is it doing so in the right way? So focus on in fact, understand the context, be fair, but really go all and make a difference, because data science we're ready for that. Thanks for being on data framed. No, thank you. Thanks for having me. You've been listening to data framed, a podcast by data camp. Keep connected with us by subscribing to the show in your favorite podcast player. Please give us a rating, leave a comment and share episodes you love. That helps us keep delivering insights into all things data. Thanks for listening. Until next time,.

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