39: Real World Data. Real Life Results.

See you now as a podcast highlighting the innovative and human-centered solutions that nurses are coming up with to solve for today's most challenging health care problems. Created in collaboration with Johnson & Johnson and the American Nurses Association and hosted by Nurse Economist and Health Tech Specialist, Shanna Butler. There are so many patients who present for cancer care who don't fit into the cookie cutter box of who would qualify for a clinical trial. We have really great data and we have talent to really important things and how could we harness those things to actually look into disparity in cancer care with some gusto. Welcome to See You Now. I'm Shanna Butler. The use of data is rapidly shaping and transforming every aspect of how we measure, track, research and deliver health care. Using large data sets created from health records, insurance claims, patient registries, wearable devices, ambient sensors and more, innovators are unleashing an interesting range of technologies like artificial intelligence, machine learning, natural language processing and sentiment analysis to open up entirely new avenues to understanding health conditions, how they arise, how to address them and possibly even how to prevent them. Cancer is one area where the use of data is rapidly transforming every facet of care from discovery, clinical trials, treatments, surveillance to prediction, prevention and control. And one rapidly evolving development is the use of real world data to provide important insights, understanding and relationships that are often not answered using data from the highly prized clinical trials. One very specific area that data, particularly real world data, is helping us to understand is how race and ethnicity play a role in disparities in care and outcomes. In this episode, we go deep into the data weeds with a clinician, innovator and data specialist to reveal what stories the data really has to tell. Hi, my name is Kathleen Minnan. I'm a college nurse practitioner and I serve as senior clinical director at Flatiron Health. Flatiron Health is really a combination of two different businesses. There's Flatiron HC, Flatiron Healthcare. We support a network of community oncology practices and we do so through a number of mechanisms, but the most important one is we support a e-medical record called Uncle EMR and the cloud-based e-medical record that is used in all of the community oncology practices across the country within the Flatiron Networks event, ends up being about 260 plus practices that support 2.5 million patients. So that's one side. The other side is the research side. On the research side, we actually take the data that comes in from those community oncology practices via Uncle EMR in addition to information that comes in from our academic partners and we take that data, de-identify it, and we follow patients longitudinally from the beginning of their cancer journey all the way to the present. As of 30 days ago, this how recent our data is. This summer, we launched a team looking at race and ethnicity data for the sake of examining healthcare disparities in oncology treatment. I leave that effort along with a fabulous team of other nurse practitioners, operations, folks, and statisticians. Kathleen, you have an extraordinarily interesting background that's not particularly typical for what we see in healthcare. I'm just sharing a little bit about the role that you have, how you got there. This wasn't planned for you, was it? This was not a career by design. No, no, and I'll tell you that divine intervention following your instincts will lead you in places you never planned. It will give you an outcome that's so much better than you could ever planned on your own. I've learned a lot from just going with the flow, but I'll tell you one where I am now. Shana, I have the best team. I can't even, there are no words to describe. Brilliant, brilliant women who are intrepid, who can just think through things in a way that I can't. I mean, we work together like Valtron, right? Valtron has this super power and you come together and they make magic happen every day. For me, it's the ultimate privilege. I'm going to see your clinical director at Flatiron Health. I work on the research side of the business, but how I got here was through a bunch of twists and turns because I was really determined to get a farma job and an opportunity came up via a headhunter to work at a tech startup called Metadata. This is when Metadata was legitimately a startup, 200 plus employees. They were one of the first companies to take the very paper labor intensive process of clinical trials data compilation and bring that completely online and build electronic data capture systems. I was super intimidated because I hadn't ever worked in tech before, but it sounded interesting and it was in a cool part of town. I worked at Metadata for about a year before I eventually got recruited to go to Roche to help them build one of their first homegrown EDC electronic data capture systems. When I got to Roche, this idea of nursing school was still kind of brewing in the back of my mind. Was it going to let you go? No, and talk about magic. Roche had a relationship with Fairleigh Dickinson. Once I was full-time employee, I was able to go to nursing school at night on the weekends and Roche helped pay for that. They actually made it possible for me to go to school. I was in my 20s, so going to school full-time while doing clinicals and working full-time. I don't know that I could do that again, but it was such an amazing opportunity. When I finished, I ended up starting my nursing career and I worked in infusion, ICU, and then a former attending that I worked with as a research assistant came to me and said, "I'm starting a new practice that's a lymphoma center. Can you help me start this?" We have an infusion center. It's brand new. I need someone who knows research and infusion, critical care to help me start my infusion center. It's great. The way he described it was just beautiful. Of course, I get there. There's an empty room. When he said he was starting, he literally meant to be here. Really meant it. If I had known that I was literally walking into an empty room, I would've been way too intimidated. I don't have that kind of experience. I don't know how to start an infusion center, but I was already there. I got a catalog with chairs and pumps. It ended up legitimately starting this practice from scratch. Train the nurses. I wrote the chemo orders. I gave the chemo. It was around that time I finished my NP. Running an infusion center, seeing patients in practice, training, it was a 12-hour day job of spurtle. I had my first paid disparity drama. I went over to Cornell, worked in bone marrow transplant, and then I got a call from a recuda in Flatiron. She says, "Well, we're looking for a nurse practitioner. We're looking to more experience to help us expand our clinical team on the research side." I said, "No." It wasn't interesting. I felt like I had left my data life behind, but apparently there weren't that many nurse practitioners who had worked at tech startups in Pharma. So she asked her a favor. She said, "Would you just interview with us?" Just so I could say I'm doing my job, I said, "Okay, that's fine. Half an hour." Zoom call. Clearly something clicked. It was the first time that I had met a group of people who were trying to solve a problem that I tackled every day in my clinical practice. And that problem is, what's the data? Thank you all. They're too young, they don't have great kidney function, great liver function. Maybe they live too far from the hospital. There's so many reasons why they're not eligible to enroll in a trial or just not capable. Those patients, we don't know what happens to them. We don't have great data. We don't have papers to support treating one way or another. And so those decisions, those really critical decisions oftentimes come down to convenience. Who's the attending that day? Who's allowed his person in the room? Who had one anecdotal experience with a patient that maybe was like that patient? And so the decisions that we make in cancer care regarding treatment, diagnosis, sometimes those decisions are more arbitrary than we would like to admit or that we would like them to be. I thought, there's no way I can't try to help them solve this problem because I know I face it every day. My patients don't even know that we're facing it to the degree that we are. We try to do our best, but sometimes those decisions aren't as informed as we would like them to be. And so that's how I ended up at Flatiron. So from the very beginning of your career, you have been on the cutting edge, the leading edge, the data and technology curve that is transforming cancer care. Flatiron help, what are you and your colleagues on a mission to achieve? Who do you serve? How do you do this? What is Flatiron help doing? We're doing a lot of things. Our mission is to learn from the experience of every cancer patient, which is huge. And again, because altogether, only about 4 or 5% of patients who are treated for a cancer do so within the confines of a trial. So what happens to the other 95%? That's what we're trying to figure out. Takes a lot of people. Takes a lot of nurses to help us do that. In addition to the data that comes in from these practices, we have a huge army of abstractors that consists of oncology nurses, research professionals, tumor registrars that actually go into charts and help us glean the relevant information, data points to help us weave those stories and really bring those stories to life so that we can understand more about treatment patterns, outcomes, and use that to help support ongoing clinical research that's happening as well. We also have oncotrile, so we're moving into the prospective evidence generation space. We partner very closely with Foundation Medicine to help build clinical genomic database. And Foundation Medicine specializes in next generation genomic sequencing. So we're able to link the data that comes in from Foundation Medicine with patients in our network, and we're also doing this prospectively so that we can start to, one, actually look at this in real time and also democratize the access to information like this. We're embedding a lot of this research into, again, community oncology practices and going to where patients are, where they live and where they actually seek care. So the possibilities are endless. The work is endless. When I first got there, I think the part that was just so intimidating and also exciting and thrilling and scary is the fact that there is so much information out there. It gets you up in the morning, you're like, I need to find things. We need answers, and there's almost like not enough time in the day to ever get to those things. You said a couple of things in there that really speak to the revolution and the transformation of what cancer care looks like. First of all, you're going into real-world data. You're in community centers. These are not people who are in clinical trials. You're doing it prospectively. You're doing it in real time. You're doing it with genomic-based information. All of these things have existed separately, but it's that pulling all of those strands of activity together that becomes actionable in real time that makes a difference in people's treatment. What's the impact right now of cancers on our society and our communities and on our families? One big thing. You talked about cancers with MS. Cancer is thousands of different diseases. Some of the fastest growing, the most deadly thing is known to me. And some of the most intimate, slow growing. Everyone is different. Right now to a molecular level. There's a couple of things to think about. So one, the way we diagnose and treat cancer has changed dramatically. Just even in the past year, two years, five years, how many treatments have we approved for cancer care? Hundreds. It's astounding how little action there's been, especially in cancers like acute myeloid leukemia, no approvals for 40 years, and then suddenly you've got five new approvals in the past couple of years. It's a dramatic change and that change comes down to the molecular builds of those cancers. It's not just enough to know the histology or what cell it comes from. It's like what is in the visual cells? So yes, we're making great strides, but you have a couple of problems that come with that. There's very expensive to treat, extremely expensive. And a lot of these diseases have gone from being acute. We need to treat it or you die to these are diseases that we can control that you can live with. And so knowing that you can live, you know, a decade or more with some cancers that we could not even dream of treating in any way that was effective, it's great patients are living lives, they're raising children, they're working, but they're also strapped. It's become more of this chronic condition, more so than acute. But even with that, it's a double-edged sword. Yes, we're living longer, but we're living with other issues, we're living with financial constraints, we're living with side effects, we're living with treatments that may affect our quality of life in a really significant way. There's still certain cancers that we have not moved me to on. Pancreatic cancer is a great example. You know, we've made small strides, but it's still a disease that kills about 95% of patients to get it within a few years. So we're making moves, but they're not across the board. One, just again down to molecular level. And you know, not every patient is seeing the great strides that everyone gets to experience. You can name the reason for the disparity, but we still have racial disparities that persist in cancer care. We still have not reached all the communities that deserve our outreach. For us to have made these strides and know that it's only available to just a subset of potential patients that we can reach is a really tough thing. So I think we've tried to move a needle. We continue to push. One is just by the fact that we're able to collect data in real time, and we can see where treatments are being applied, where patients are actually making progress and where they're not, who is not. We mentioned that it's uneven, that every aspect that there's unevenness, whether it's tumor type, cancer type, molecular type, all of these things, you're part of a team that's really trying to answer the questions around differences and disparities, specifically as it relates to race and ethnicity. And really trying to understand whether a person's ethnic background or racial identity, is it affecting the care that they receive, the options that are available to them? What can be done? What can we do about that? Same more about this team that you're a part of and what your team is doing. Yeah. So picture with me. It's June 2020. COVID has been tearing through the world. We are ready to start to see the disparity in who's affected, who's dying from COVID, who's not. George Floyd just died beneath a police officer's knee on video. I'm mad. There are a lot of people who are mad. Literally the world is on fire. That's where we are. And while this is happening, you're starting to see corporate messaging. Say something that folks have been saying for years. Black lives matter from flat iron as well. And yes, it's nice to see, but it's also deeply enraging. Deep. This is something that I've known. I've experienced what it feels like when Black Lives don't matter. And now to see this kind of corporate messaging, it just created so much cognitive dissonance for me as a researcher, as someone who is deeply interested in health outcomes, and making sure that everyone gets to benefit from what is coming out of this research, what's coming out of cancer care improvements over the past few years. And so I needed more than a corporate statement from Flatiron. And right away, they said, "Okay, what do you need? What do you need us to do that we're not doing now?" And the two things that I knew we had, we have really great data, and we have talent to really important things. And how could we harness those things to actually look into disparities in cancer care with some gusto? Really put money behind this and say that we support this, that we want to make changes, that we want to highlight what is happening in cancer care now. And so I enlisted a team of really great researchers that includes Kelly McGee and Laura Long, Sam Azria. We decided to really dig into Flatiron's data. Where is it coming from? How are we reporting race data? And how can we actually describe what's happening to each and every cancer patient as our mission sets forth? What I noticed when I started working with our data is that while we have a ton of patients, and we can glean in insights, and we've been able to put out some really high quality disparities research. But I think we can get more granular with what we're seeing as far as how race is reported, where it's reported, so that we can really get those insights and really power data for even smaller populations, where it starts to become really important to connect race and genomics, and have enough numbers there to get the insights that we need. Well, and the broadness of the data, these aren't just people who are in clinical trials. These are people dispersed across the spectrum, across the country, into all sorts of communities that aren't 50 miles within an academic center. Yeah. And so we've been able to conduct really great research in the past, but I will tell you a lot of that research was done in the context of hackathons. It's my colleagues and I thinking about these questions and doing it on our own time and listing our colleagues who are also really interested in this on our own time and publishing this on our own time. And I thought to myself, well, imagine how much good we could do if we were actually getting paid to do this. And again, it comes back to, we have the resources, we have the talent, let's put it together and actually power this resource with funding and time so that we can dedicate resources to this. And so we now have a group dedicated to this. It's a full-time job. We're actually recruiting ahead of research focused on equity disparities so that again, somebody can think about this. Full-time, using the millions of data points, the millions of stories that have not been told. I mean, Shawna, I can't change what happened over the past four years, 400 years, even just the past year. I can't change that. What I can do is help rewrite history to include the stories that were not told to include the folks who were overlooked. We can't change history, but we can rewrite it. How does a disparity show up? I mean, I think we think about just skin color, but there are so many different ways that you're revealing through the data where disparity shows up. Have you got some of those stories to share? I mean, I'm going to tell you a personal story. 2020, besides COVID, one of the really tough things this year is that three of my close friends were diagnosed with breast cancer. And one in particular, who she's single, she's a really talented casting agent in New York City, talented, smart, but she's freelance. So in the past year, she had let her insurance lapse and thought, "Yeah, I'll get back to it. I'll do the Obamac exchange when it comes back open." So at the time, even though this is an educated, employed woman, she was also uninsured. And so we had gone to the beach, come back, and she felt a lump on her breasts. And I said, "Well, it's not the thing you want to play around with. Let's get you set up." So she had her merogram, of course, the results were not what we had hoped for, but they were able to set her up with a surgeon and get her emergency Medicaid straight away, which is one of the beautiful things about New York that you don't always see in other states that have not expanded the ACA. She was able to get in straight away. And Shawna, it's crazy. For me, as a nurse practitioner, an oncology nurse practitioner, I have no idea what would have happened to my dear friends. Had I not been there to advocate for her? Had I not known what an experience for a new patient should be like? It took, once we saw the surgeon, he barely made eye contact, barely asked her any questions, assumed that she was unemployed because she was on Medicaid, barely asked her what she did for a lift. I mean, had really no interest in her as a person. First person checked her and, "Oh, yeah, she's on the grant. This is one of the grant patients." Uh-huh. They lost her NGS testing. It turned out she's actually Brock-apositive. So here you have someone with a high risk mutation who couldn't get an MRI for three weeks. Couldn't get an appointment with an oncologist. The surgeon said, "Well, looks like your stage three, I think you need to go see an oncologist. Here's a phone number. If we had stayed in quality instructions as they gave us, she would not have started treatment for at least a month. A death sentence for someone with a high risk mutation. We didn't find out she was high risk until we went to Sloan-Cattering, which is a totally different place. Luckily, Sloan takes Medicaid and so we went there and it was an entirely different experience, Shana. I mean, from the beginning, there was never a moment where anyone commented on what kind of insurance she had. She didn't really matter. She was there. She was able to get her scans the same day that she saw the surgeon, the same day that she saw the oncologist because they booked the appointments together. There or not she had, NGS testing was a paramount importance. So that personal experience, those subtle, nonverbal cues, eye contact, listening, active listening, what's important to you? Are you still working? Do you want to continue working? What can I do to make this experience better? That simple bit of conversation that signaled to her that somebody actually cared, cared about her outcome, was interested in expediency and getting her started with treatment straight away, made a huge difference. How can we even measure what her outcome would have been? Had she stayed in the place where she was literally just a number, a Medicaid. A Medicaid number, you're a black woman, you're on Medicaid, you're treated a certain way in certain spaces. No matter what your degree might say. To see that in real time, it was, it's a punch to the gut for me as an oncology nurse. I still think about her and I think about the other women in the waiting room who did not have a personal advocate who had an understanding of the healthcare system. These disparities show up in a lot of different ways and there are so many stories like this, so many. What I think you've done, which is quite helpful and a starting point, is not only are the stories out there, you're actually collecting the data that identifies. That this exists, how it exists, where it exists, who it happens to and more importantly, the impact that's happening. So it's that starting point of, let's, rather than me just share this story that was my experience, what does the data show us? Where can that be helpful? What data are you collecting and what is it revealing? We want to see everything that happens in a patient's journey. So starting with the basics, what are the demographics? Right? It sounds really simple. Age, where you live, who's paying for your care, whether it's a commercial insurer or a government insurer, your race, as you report it, all of that is there. And then we're looking at what treatments did you get? What are the interesting characteristics about your tumor type? When did you start treatment? When did you end treatment? When did you, if you passed away, when did you pass away? Were you treated before you passed away? What kind of supportive care have you gotten over time? And when you drill down to actual treatments, like, are you getting standard of care? Are you on a trial? Are you getting something novel that was just approved in the past year or two? Are you getting the opportunities to receive the best that's available right now? And I'll talk briefly about our Asco plenary last year. Probably one of the more exciting experiences we've had collectively at Flatiron, the American Society of Clinical and Collegy hosts a conference annually. It's the largest cancer-focused conference in the world. On average, they have about 40 to 50,000 people attending annually. And out of the thousands of abstracts that are submitted each year, they choose four to highlight at a plenary session. The plenary session means everybody pencils down, everyone pay attention. This is something that we want to talk about. And we did a study looking at the Affordable Care Act expansion. And so we looked at the difference in time to treatment for patients that were either in ACA expanded states. Four states that did not participate in Medicaid expansion, aka Obama Cares. A lot of people know it as. And what we found is that for states that did indeed expand Medicaid access, time from diagnosis to treatment shrunk. The disparity was eliminated. Eliminated? Wow. So going back to my dear friend who she was able to start treatment because she had Medicaid. And again, I think about it all the time. What would happen as she lives somewhere where Medicaid expansion hasn't taken flight? And we can talk about all the historical reasons why it hasn't taken flight. But I won't do that today. But these are the kinds of questions that we can answer with our data. We can look at not just when you're getting treated, but what are you getting when you get treated? So one of the early studies that I did, I looked at Multimiloma. And there was a new drug that had just gotten approved in 2015 just a few years ago. And I was interested to see just because of anecdotally what I was hearing from certain patients who was actually getting access to some of these more novel treatments. So I chose a drug that has a really nice side effect profile in that patients who have trouble with their kidney or liver can receive this drug pretty easily without it causing further damage to those organs that might not be working so well. And when we looked across the board, African-Americans were less likely to get this novel therapy. That's across payer differences, differences in region of the country. And I can't tell you why, but I can tell you that sometimes there is a bias when you have a patient in front of you. Every clinician struggles a bit with bias in their day to day care. You might have a patient who looks like your favorite aunt. And so you get her that extra cup of juice or you have a patient that reminds you of the bully in high school. But you can feel those things viscerally and you can react to them in real time. But sometimes when you have a bias against a group of people, it's really difficult as a clinician to accept that maybe you might be treating them differently. We take an oath to do no harm. We know that we want to do our best and it's really hard to look in the mirror and say, I may not be treating my patients equally. So when you look at these numbers and you can think back to your experience and think back to how you make treatment decisions for your patients, it's really critical to have those numbers in front of you so that you can kind of think back to a time where maybe you had the option and you didn't use it. We can talk about this anecdotally all day long. But the data is in black and white and it gives you something to react to. It gives us something to talk about. It gives us an opportunity to address different ways to treat patients that are within standard of care but take it up a notch to something a little bit better. I mean, real world data, now I'll tell you what it can do. It can't replace a randomized clinical trial. It just can. There's still incredible value in randomized clinical trials. But what can it do? The answer is really our endless. We haven't figured out all the right places to put it. But real world data can tell us a lot about what's happening to patients in almost real time and allow us to pivot quickly. We've seen the value, especially in the past year with COVID-19. We were scrambling. And it feels like you're building that foundational layer where we have the data to help us tell stories and then people who have these stories, now they have data that can back it up. We used to quote to really highlight what we were trying to do in examining our racial disparities and the quote that I tapped into was not everything that is faced can be changed but nothing can be changed until it's faced. James Baldwin. And it's sometimes it feels like I'm not doing enough when I was in the patient room with my friends angry. I thought, my gosh, I should be in the clinic. I should be part of this solution. And I realized my role in being a part of the solution is much more broad. It's being able to compile this information. Have us look at it. Take a hard look so that we can actually do something about it. As a nurse, sometimes it feels like you're not the person people are looking to solve these problems. It feels like we're going to go to the position we're going to go to the academics so they can start to talk about these things and they can teach us how to do things. But we are the front line. We are the ones that actually talk to our patients, teach our patients, really bring them along for the ride so they understand what's going on. We are a critical piece of this. We're a critical piece of innovating ways for us to look and see what it's going on day to day in patient care. You mentioned that you're sharing this data with clinicians and that it can be really hard to look at. So how does having the awareness of the data, how is that changing the clinical behavior, treatment considerations, and more importantly, the patient outcomes? I'll start with me as the end of one. Before when I got to flattered, I assumed that my work as a clinician would inform my work as flattered. That was what was going to help me ask the right research questions, craft the data and which data points we collect. That's what I thought was going to happen. It was going to be this kind of one way flow of experience and information. What ended up happening was exactly, I can't say it's exactly the opposite, but looking at data from this bird's eye view has really changed the way not just the way I communicate with patients, but also how I think about what it is that I'm putting into a chart in a way that I never thought about before. How can I optimize the way I tell a patient story so that I can then compile this so that patients who are like the woman in front of me, the thousands, possibly millions of patients who look like this person from an appearance standpoint, from a molecular standpoint, how can I make sure that I'm treating them the best that I can? And a lot of that comes down to what is the data that I'm putting into this chart. Real world data is only as good as what we put into it. And that's not something that I think we're consciously aware of as providers, you know, in a clinical trial. Yes, of course, this data manager that told you what to put, where to put it, how to put it, even something as simple as performance score for patients to come into the clinic until nurses, doctors, anyone who interacts with a patient chart starts to really take key to how powerful that data is, we're not going to treat it that way. But imagine if we had the mechanisms of some place, enable real world data at scale worldwide. That that's been a fascinating thing about COVID in the pandemic is that we've seen the impact of real world data, how collaboration, partnerships, sharing, you know, making sure that people have access to it. It is on a day to day basis, changing our recommendations about treatment, about screening, about isolation, quarantine, travel, mask wearing, you name it, you know, and that has not been driven by the gold standard of a controlled, you know, randomized controlled trial. That has been real world data. Yeah. I mean, COVID has been terrible for so many things, but if anything, good, that's come out of it is there is an awareness that real world outcomes, real world data has a huge impact, has huge opportunity. My focus is cancer care, but obviously COVID has touched all of us. It's greatly affected cancer screening, cancer treatment, cancer outcomes, and we're actively investigating that now. I wish that I had a cache of MPH trained nurses to hire at any given point. We've built our team to 13 MPs, they're fantastic, they're brilliant, and they've been able to really pivot their careers to learn that's on the fly. And when you're sharing your journey, it doesn't sound like in that early stages, there was ever this sense of, I want to be an innovator or I want to be involved in an innovation agenda or technologies or new ways of treating people, you know, thinking about how are we fundamentally transforming care, how are we using data in order to be able to do that. So over this career experience, I would imagine you've developed an attitude or definition or a philosophy about innovation. What is that? Innovation for me is looking at problems in a new way each day and thinking of different ways to solve them that we maybe haven't entertained before. Something as simple as finding a way to stop a door to finding ways to treat cancer that we have been entertained before. We can always do this better, always. This always a better way, Sean. The one constant in my career, every pivot has been around change, right? Things getting better. I worked in clinical research. Whenever I saw a research assistant in 2001, 2003, I used to fill out paper forms in binders and fax them to a pharma company where two people would do the data entry on one sheet. They would query the errors between those two. Then there would be another series of queries. Something that the process was archaic. So my first foray into tech startups was just in thinking things off of paper and making it electronic. I had a sense at the time that wow, this is so much better than what we're doing now. At that point, you kind of have to think like there are patients right now who we know are not doing as well. For reasons that have nothing to do with their tumor type or when they were diagnosed or any of those things, how can we make those outcomes better? I know we could do better. I know that as long as somebody dies from an illness that we've known about for decades and decades, there's an opportunity for us to do better, always. You know, oftentimes I think we hear the word cancer and you can just hear the deflation. But when I hear you talk about it, I don't feel that way. I feel like there's a lot of stuff that's possible here. We haven't even scratched the surface. I mean, imagine if we, collectively, and yes, Flattern is a big part of this, but in a real world data, I see more and more adoption across the board. I see new companies popping up. We do a lot of data abstraction, but machine learning and artificial intelligence is moving the field forward in ways that even I couldn't imagine four years ago with that opportunity comes a lot of responsibility. I think we can do a lot of harm if we don't do it right. No, we need to be thoughtful about our approach, but the possibilities are endless. Kathleen Minyon is a senior clinical director on the research clinicians team at Flatiron Health, where they are using massive amounts of data, sophisticated technology, and the experience of every cancer patient to accelerate cancer research and improve care and outcomes for every patient. Kathleen leads an incredibly talented team working to answer questions about race and ethnicity. Whether a person's ethnic background or racial identity affects the care they receive and what if we knew about those differences, we can do about it. Kathleen comes to this role with an unusual and perfectly suited background that includes hematology oncology nursing, clinical research, working with scrappy health data startups and well-established bio-farmer players. Kathleen uses her expertise and obsession with data to ask and help answer important questions pertaining to race, ethnicity, and disparities in care. Kathleen also continues her clinical work as a nurse practitioner focused on bone marrow and stem cell transplantation. Researchers and clinicians have had strong suspicions of the power of real world data, and COVID has helped confirm it. Their ability to produce, analyze, respond, and incorporate real world data into practice research and policy has been stunning, pivotal, and illuminating. And with due diligence, real world data can be used to identify and help close gaps in healthcare disparities. As Kathleen states, real world data does not replace clinical trials data. Well, they really are the gold standard that significant advances in clinical decision-making relies heavily upon can be slow and costly, often produce results that are narrow to apply, and can be quite difficult for patients to participate in for any number of reasons. However, our growing use and confidence in technology and digital data combined with the dynamic policy landscape have created fertile ground for the use of real world data in creating new awareness, guidelines, and understanding of a broader dimension of care, treatment, and outcomes. Real world data can support insights into the natural history of disease, how people are diagnosed, where people get their care, who gets offered what treatments, when those treatments are started, and if they're completed. How people tolerate and respond to treatments, who pays for treatments, and as Kathleen highlights, if there is any bias that affects the care they receive. Kathleen and the race and ethnicity data team share that it's critical to have those numbers in front of you, to give you something to react to, something to talk about, and an opportunity to do better. Real world data can tell us a lot about what's happening in almost real time, and if need be, to pivot, to course correct where necessary. When you ask Kathleen about the possibility real world data offers, her answer, the possibilities are endless. We have yet to scratch the surface. I'm Sean Abeller, thanks for listening. [Music]