Research in Action

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How can an extensive collection of real-world data help find diverse participants for clinical trials? Are some organizations already using the concepts of a continuously learning ecosystem. And what are the biggest remaining challenges to patient record standardization and personalized health care? We'll find all that out and more on today's Research in Action episode.

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Hello and welcome to Research in Action, brought to you by Oracle Life Sciences. I'm Mike Stiles and our guest today is Dr. Lu de Souza, vice president and executive medical officer of the Learning Health Network, which is a division of Oracle Life Sciences. In a nutshell, Dr. de Souza leads a team that seeks to help health organizations integrate clinical research into everyday care.

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That means addressing clinical discovery, cost time and patient inequities. She's also a huge advocate for real-world data, bringing technology to bear for true healthcare advancements. Dr. de Souza has years of experience in health informatics and was the most recent CMO of Cerner in North America. She practiced pediatric hospital and emergency medicine until 2020 and has held multiple leadership and teaching positions.

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Dr. de 'Souza, thank you so much for taking the time to be our guest today.

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Now Thank you, Mike. It's really a pleasure to be here. And please feel free to call me Lu.

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There's a lot of ground to cover here. But first, let's just find out about you. What was the life path that brought you to where you are today and doing what you're doing today?

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You know, as you mentioned, I am a pediatrician who focused on taking care of sick kids in the hospital and the emergency department. And I really loved my job. But like many doctors, I felt frustrated by the inefficiencies of health care. And I felt very frustrated with the limitations of time and data that we suffer both of those things are super essential to make the fast decisions that we need to make.

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So I started thinking about technology and the role that it could play in solving some of these foundational issues. And also, you know, we always want to see how many more patients we can help. So I felt like the pivot would allow me to take care of patients in a different way, but at higher numbers. It was not easy decision.

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It was very hard for me to leave full time pediatrics, so much so that I stubbornly continue to practice for the first ten years that I was full time at Cerner. But at the time that I was considering joining Cerner, my mother's breast cancer was misdiagnosed and that happened because of inequities, fragmentation in care and a lack of standardization that exists today.

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Eventually, she turned out okay with that. But these missteps and delays in diagnosis led to a much more aggressive course of treatment and the complications that came with it. But this experience really sealed the deal for me. I felt like there was a lot of work that I could contribute to so that led me to my career in informatics that started with EMR implementations and technology enabled process improvement.

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Then ten years later, my cancer warrior mom was diagnosed with a different cancer. This one was rather rare and aggressive, and we quickly found that there was not enough research to support any specific type of treatment for her and that the survival rate for anything that they could try was pretty low. And that was not good enough for her.

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She decided to forego treatment and instead focus on having better quality of life for the remainder of the year that she was with us all of nine months. In stories like that, Mike, are super common. Many of our listeners, I'm sure, have gone through something like it and as devastating as it is, these life experiences also help shape us and they bring these opportunities that we hadn't considered.

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And sure enough, only a few months after her passing, the Learning Health Network was founded and I was asked to help out and I was immediately drawn to its mission and vision and the impact that it could have in cases like my mom's. So it took a little bit of time to get here. But last year I was able to take on a full time role with Learning Health Network, and I'm just super excited to be a part of this awesome team that brings transformation to research.

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Okay. And tell us what the Learning Health Network is.

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All right. So I'm going to start with the why and why it was created and paint this picture for for everyone to understand how important this is today. Clinical discovery. So how we get to medicines and treatments and different diagnostics is still a major challenge for life sciences and health care organizations. And because these two sectors of our industry are mostly siloed from one another, it's a very onerous process for patients and providers to participate in clinical trials.

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Even myself as a doctor who understands the language of medicine had a really hard time finding out what types of trials were available to my mom, just as an example. So for context here, when we're bringing a new drug to market, it takes approximately 17 years and it costs an average of $2.5 billion. That those are crazy numbers, right?

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And the biggest driver of that time and cost is getting to the patients, identifying the right patients, recruiting them and enrolling them into these trials. And about 20% of these clinical trials fail because they cannot recruit enough patients. And overall, only 3% of our population participates in these studies. Of course, 3% of the population cannot be representative of the diversity that we have here in United States or across the globe.

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So the Learning Health Network was created to help solve these problems with the concept of these patients are in everyday care, and that's where trials need to go. We need to bring research into everyday practice. The Learning Health Support Network is a partnership between Oracle and health systems that we serve, and these organizations contribute their de-identified data to serve as the fuel for research and clinical discovery.

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So this data set is called the Oracle Real World Data, and I'll call it our RWD from now on to to make it easier. And it's one of the largest datasets in the world like this in exchange for that data contribution, which we're immensely grateful for, Oracle provides these organizations the access to the data set so that they conduct they can conduct their own research, and we provide that at no cost.

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We also do all of the heavy lifting for them, so it doesn't take any effort on their side to get the data there to make it de-identified and normalized. We do all of that work and then we offer a variety of benefits for them depending on where they are in the course of doing research, whether it's data science or clinical trials and so on.

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So the Oracle Real World Data is home of about 108 million active longitudinal records from all over the United States, covering about 2600 facilities. And this membership comes from a variety of organizations. These whole systems can be large, multistate and academic centers all the way down to critical access hospitals. And this combination, this this composition of membership is intentionally done and balanced by us.

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So they're very similar in numbers. And that becomes our superpower by having data from such a wide range of facilities and such diverse communities, and means that people who never had access to clinical research near their homes can now be represented in this dataset and represented in a lot of research that gets done. And it also means that this research, a big data set, matches fairly well to the US Census and brings that much needed diversity that we're lacking in clinical trials today, and that helps decrease the the health and research inequities.

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How we do this is again, by using the dataset to find the patients. So we find patients that are good matches for trials, and then we find trials that are good matches for those sites and for that community. And the data can also be leveraged like I said before, by organizations to drive or derive clinical insights by using data science and the tools that Oracle provides.

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That is us in a nutshell.

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I think there's a lot of people listening that would be really surprised to find out the thing that slows down getting new drugs and new treatments to market isn't necessarily like bureaucracy or red tape or lack of scientific knowledge. I think people would be surprised to find out the real problem is being able to find and get people and a diverse group of people to participate in these clinical trials.

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So that's probably what adds great value to this dataset, right?

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Yeah, I mean, the things that you mentioned definitely are barriers that we have to cross as well. But it was surprising to me as well as I entered into this space. Just as an aside. One of the reasons it's so important for clinical research to be embedded into care is because we people, patients, we trust our health care providers.

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You know, these are the people that we listen to and take advice from. So the studies have shown that the majority of patients that enter clinical trials or accept to participate are because those trials were discussed by their providers.

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And what's your role in it? What what constitutes a really good week or a month for you?

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As the executive medical director, my main responsibility is really to the health system. Members. I have a team, a super awesome team of clinical researchers that ensures these members gain value from their incredible data contribution and also know how to leverage it. We provide programing around them so that they can learn, collaborate, network and so on, and I also lead our clinical research strategy and operations, which is focused on two major components.

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One is bringing the funded research opportunities to the members that want to have clinical research research programs, funded opportunities, meaning they come from life sciences organizations and cross, and also helping these organizations that are smaller to become research ready. So these are organizations that don't today have a program or are beginning and they need more support. The second major focus is breaking down the silos that exist today between clinical research and care delivery, and that will help drive the awareness, the efficiencies, the safety.

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It will help us improve that patient recruitment into trials and so on. Now, boy, my my day to day changes quite a bit. So a good week or a month is hard to describe, but I would tell you that a really good day is when one of our community, Rural Health Hospitals, is awarded a study that we facilitated.

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And because we know that those patients will be represented, that community will be represented in research and they will gain access to cutting edge medical interventions. It feels really good to know that we played a part in that and another really good day is also when our members use this data set to gain insights that lead to positive patient outcomes and that we're blessed to hear about that quite often.

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Our Learning Health Network members have published over 500 peer review articles using this data set.

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Best case scenario if the Learning Health Network gets its job right, how can that change how health care data, The gathering and use of real world data is used to improve patient outcomes and health care policy?

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Yeah, I would just reiterate a couple of things. With the Learning Health Network and its real world data, we'll have real data in real time deriving insights to lead to better care and better outcomes in the continuously learning ecosystem. We'll be able to quickly restudy and improve upon those longstanding medical practices we have today. So the word restudy is really important because we do have a lot of medical practices today that are gold standard and they're based on old research or based on research that didn't include certain populations, didn't include the necessary diversity or, you know, certainly the composition of us as human beings has changed.

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So it is very important to ensure that we're still providing the best care and we can use the data for that. And that also will decrease these existing disparities and drive us closer to personalized care. The future also would look like we no longer will take so many years to complete clinical trials because we're going to know where the patients are for specific studies.

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We're all going to know what those studies are more important to take to specific communities and patient populations. And and I think that is going to alleviate a lot of that, not just the time, but also the cost, because these costs are, you know, also what driving the cost of medications for our patients or interventions. Let's see, we'll be able to get to a more predictive and prescriptive models of care.

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So understanding not just what happens with an individual now and how to take care of that problem, but also understanding what's likely to happen to Mike based on data points that we have on you today and behaviors. And this way we're able to intervene in the product in a proactive way. Imagine being able to predict and prevent a heart attack from happening three years from now.

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All of these things are in our reach today. And the good news is that we're not too far from them. In fact, our our member organizations, the ones that are using the the real world data, are already experiencing practice and research transformation. But we certainly need to scale this up, scale this approach, and hopefully we'll get to a point in which the medical community will trust more on approaching research in this way and it becomes more the standard of care of how we discover and apply changes.

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And I also think there is going to be a lot of other possibilities of this data set brings that we haven't necessarily conceptualized yet.

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So follow up question You mentioned that organizations are already doing this. Can you give us an example or two?

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Sure, sure. I'll give you two of my favorite examples, not just because I'm a pediatrician, but also because less than 20% of all U.S. research funding is dedicated to children. This is a highly underrepresented population in research, just by sheer numbers, which means that patient recruitment in trials is even harder. And conducting those trials in the traditional way is much more challenging.

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So these two examples come from very proliferates users of real world data. And in these are pediatric hospitals. The first example comes from children's health of Orange County in California, where they have used RWD and machine learning to create what is the first published pediatric readmissions algorithm. So it's an algorithm that gives us a risk of readmissions for patients that were in the hospital or presented to the hospital, and they were able to accomplish that in the matter of months.

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They then incorporated this risk score into the clinical workflows. They put it right inside of their Oracle, Cerner EMR, and they saw a 10% decrease in readmissions in the first two years, which is just commendable. You know, it doesn't just improve the quality of of these kids, but in today's healthcare, this change also amounted to $2.7 million in cost avoidance.

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Everyone knows how expensive it is for hospitals when a patient is readmitted. The other example is Children's Mercy Hospital. Their research team leverages the rural data for a lot of projects, and this one is really near and dear to me because I worked in the E.R. with children. They looked at adolescents with migraine headaches that were presenting to the emergency department with these headaches and how they were being treated.

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And what they found is that 23% of these kids across 180 AEDs were receiving opioids. I want to repeat that because that's really important to us. 23% of these children were repeating were receiving opioids as the first line of treatment, and that is not necessarily the best treatment for them. It is a misuse of the medication. And it's very aggressive.

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And, you know, we're having already opioid crisis in this country. So then they they took that learning. They created a new clinical protocol and a clinical decision support tool that they incorporated into their Oracle, Cerner EMR, and were able to decrease the use of opioids for this condition to almost zero in their emergency departments. They had several in Kansas and Kansas City, Missouri, and just like, you know, a true learning health network, they they took this knowledge and the new clinical protocol and they presented that at headache conferences around the country.

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And they know and and they're helping improve care for kids everywhere. So as you can see, the Learning Health Network is really a game changer for these organizations. They're now able to do research in a fraction of what it would be a typical research time.

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That's really exciting and inspiring because you listen to every opioid addiction horror story and they all start out with an accident or a headache or a quote unquote, legitimate use for opioids that then turned into something worse later. So that's a particularly incredible impact you're having, but I'm assuming it's not that easy. So what are the biggest challenges to making the dreams you just outlined come true for society?

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Yeah, you're absolutely right. We come across many barriers. But the cool thing about this team is we we don't find them discouraging. We're truly motivated to look for solutions in innovative ways, and we find partners that can help us as well. One of the biggest challenges of community based research is the lack of resources and infrastructure today that would allow these providers to offer trials and to conduct trials as a care option for their patients.

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You have heard this in many other ways from other people of just how burned out providers and clinicians nurses are today. They're overwhelmed by the numbers. They don't have the time and support to then take on something else like research. So we try to overcome that in a few ways. Obviously, as a software company, we're continuously looking for ways that technology can support these gaps, but we also work with outside partners who can provide the actual resources or boots on the ground and expertise for these community providers to do research.

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Another challenge is on the data and technology side, and that is that big data requires significant compute power, know it needs specialized tools, and you need specialized training. So it all can sound easy, but it's not easy. Fortunately, Oracle is the leading provider of cloud infrastructure and services. This continuous pursuit that we have for autonomous databases and low or no code applications, I always struggle with saying that these tools, it really lends itself nicely to the work that we're doing with RWD and I think it's going to allow us to challenge the market with the new generations of these data sets and tools.

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And then lastly, I want to touch on on cybersecurity, because that is a constant challenge across healthcare and obviously our entire business is data. So we have to be very aware and cognizant and careful of it and again, I think the unique to Oracle is this ability to leverage other data security experiences that Oracle has. So, you know, Oracle has been protecting the data of the financial and banking sectors for many years, and we're able to leverage that and bring that into Oracle Life Sciences as well.

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It's it's a level of security and governance to healthcare data that, you know, is really important to have and it feels good to have it.

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Well, none of this happens without tech knowledge is that have come onto the scene. So first, let's talk about how far we've come. What is today's state of electronic health records and data analytics where patient care and health care delivery are concerned?

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Yes, this is every doctors favorite subject to the notorious electronic health record in the life that I've that I've led for the last 12 years. You know, my as much as the patient records are still fragmented and EMR is are still considered clunky tools, I do think it's important to recognize the progress that we've made and the effect that it's had for us as a society.

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You know, most people's records are digitized today. You know, there are many children that are born across the world that will never have a paper record, will have their entire record available electronically. And that means that their data is available to us and it gives us this ability to understand health care like we've never had before. But of course, our industry is challenged.

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We still suffer from a lack of standardization in various areas and that makes data extraction and its use challenging in various ways. The way that I think about it, the simplistic way I think about it, is that old ATM cards, you know, remember how they only function in a specific bank and then years later you could use them within a network as long as you went to that particular symbol in the back of your card.

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And then now here we are being able to access our banking information and our money everywhere in the world. And when you are anywhere and you swipe that credit card, the transaction is seamless. I mean, it's seconds there and they're doing a lot with those seconds. You know, they're checking, do you have the right funds? Are you the right person?

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Because, you know, could this be fraud and then authorize that? So it's very impressive, their journey. And I'm sure that getting there was not easy nor fast. So similar to that. Our struggles with patient records are similar, but we've made good strides in interoperability. I think that right now we have the right direction and the right tools to get there.

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And also, you know, we have the experience from from from these other industries that will accelerate our progress. I think, you know, one of the things that impressed me when we joined Oracle is the number of the number and the variety of industries that this company supports and partners with. And I've seen this constant pursuit of working across the verticals, looking for opportunities to learn and collaborate and understanding that we're better, faster together.

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That's really important for us in health care because we do have this reputation of wanting to work alone and being difficult to work with. But, you know, when you look back over time, I don't think that we would be as well positioned as we are today with patient safety, for instance, if we hadn't leveraged, you know, the learnings and the experience of the aeronautics industry.

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Right? So flight safety and those concepts were applied to to medical safety, and that's really propelled us ahead. And so I'm looking forward to continue to work across these different industries.

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Yeah. You know, when I've asked other guests who are engaged in clinical research and recruiting for clinical research, one of the things they seem least impressed with is how spread out varied, disconnected patient records are. What's the ideal state, and can existing tech get us there, or do we need something more or is it more of a policy and bureaucracy problem?

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I think the answer is yes. You know, expanding a little bit more on that fragmentation of of record of patient record health it’s still, like I said, struggles with standardization and that's the piping and the backbone that supports good technology. So we're talking about standards for health data elements, meaning having the same names, the same codes, the same ontologies, and also standards for quality in health care data is still not universal, which is, which is a big challenge.

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So I have this colleague that works in data quality and runs a company in data quality, and he always says, you know, garbage in means garbage out. So when data is not captured appropriately, it's output is harder to use. Another big challenge is getting to a single longitudinal health record, because we do in this country suffer from a lack of a universal patient ID So interoperability is extremely important, but it's still, you know, having some difficulties getting there to a seamless in a seamless way.

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But once again, we have made a lot of progress. You know, I think that we're going to be in the place where, you know, you walk into any facility and you can scan your card or maybe you're going to have a chip on your on your arm there. Mike, I don't know. And those health care workers are going to know who you are and they're going to know how to take care of you.

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So I do believe that we are going to get there on the policy side and, well, both research and health delivery are super highly regulated and rightfully so. We want them to be, but they're not always congruent. And there's definitely increased recognition that some of the policies, regulations that we have in place are outdated. We have evolved since then and they need to be reconsidered.

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And we're seeing movement across federal sectors, like in I like the NIH and the White House to try to help some of these regulatory burdens. So we absolutely fully believe that your observations are right, and this is a great opportunity for us to help break down those those issues. And to me, that's one of the most exciting ways that we can make an impact.

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You know, we've talked to several guests over past episodes about personalized medicine. Obviously, we don't get anywhere near personalized medicine without real world data. What are your thoughts about what the true barriers are to personalized medicine? Can we start looking for it and getting excited about it? Or are we still like a Star Trek distance away from it becoming reality?

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Well, I funny that you mention Star Trek because I am a big fan and I still do. I still want to be Dr. McCoy with a tricorder. One of these days.

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I think we all have dreams.

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I always felt that watching sci fi movies is is a great way to imagine what the future can look like, like Judge Dredd and the Flying cars, you know, other industries already applying intelligence and suggestions. There are way ahead of us and these suggestions are derived everyday from everyday interactions right? You are constantly bombarded by ads that relate to a conversation you had with your spouse near a smart home device or via email or a search that you did.

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So all of this is possible. It's very personalized, but health care data needs to be very protected. So I do believe we should be able to get there to more general personalized care, and the data is the foundation for that. There are definitely sectors or treatment areas like oncology, immunology, where these advances are already there in place. And we know more about genomics and other omics and we know how to target treatments for those patients.

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So we are we're definitely getting there.

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And thinking just about the Learning Health Network What do you see as the biggest opportunities for that organization? What does that look like in five years and what does it need to focus on to get there?

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So I'll touch on three very important things for us. And I and I think, you know, that the ranking might be different depending on who you ask on our team, but global expansion is definitely a top priority for us. We want our RWD to power research all over the globe. We want to be a part of that movement and we want to facilitate that movement.

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Extension of our data set is going to be very important and also with that extension of our platforms and our partnerships, we feel that there are many possibilities here to augment the current research and discovery processes with different types of data. We know that what makes up an individual and an individual's health, you know, only 20% of that is is health care data.

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And what we do in hospitals and in practice, 80% of that is is more related to social determinants of health and our behaviors. So there is other data that we need to bring in as well to help that discovery in that personalized care and then leveraging the rural data to support other important initiatives is very important to us.

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So rural data can help us leapfrog the current technical abilities that we have. I truly believe in AI and I know that our customers are dying to have that. So is that, you know, the easiest example I can give you that we need real data, real medical data to train AI and to create large language models that are more suited to health care.

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And then, of course, we'll continue on our mission to to bring research into everyday practice.

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With technology playing an ever increasing role in health care and how we deliver that health care to society. More of the focus does seem to be on landing on what role companies like Oracle can play. So I suppose my question is just that what's the appropriate role for a company like Oracle? What can it best do to shape the future of health care?

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Well, I certainly don't want to simplify it. And, you know, I feel like we can we can do a lot here and and really make a big impact. But I feel in its most simplistic way that companies like ours are pivotal in enablement, in innovation. We have all the tools, advanced health care, we have experience to bring from other sectors and success in my mind is is not just being creative in building things that we think are cool tech, but, you know, really partnering and listening and understanding what clinicians and researchers need in solving for the right problems.

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So that's how I see us as the conduit to get there.

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Are there any really innovative products you're kind of seeing at Oracle that are especially relevant to the work you're doing and the goals that you're pursuing?

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Well, I'm not going to lie. I am super excited about AI and how Oracle is applying AI to remove burden from health care. As a physician that suffered burnout in medical practice, this work is extremely important and it's also happening across life sciences, Oracle life sciences. So this is intelligence not only to decrease the huge amount of duplicative work that exists today, but also to be able to digest the overwhelming amount of data that we have in healthcare and provide more guided, guided decision support to clinicians and researchers and overall to improve safety for our patients.

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I think that you had a chat with one of my colleagues who was working on the life sciences safety aspects of our work, and we are leveraging AI there to help read through tons of medical records to pick up those essential elements that are needed for Pharmacovigilance. I also wholeheartedly agree that employers, as often as they are today, should be a thing of the past and that health information needs to live in a different layer, needs to be more flexible, more usable for our patients, for our providers, and certainly for health delivery systems.

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So Oracle is currently working on that and that's going to have a tremendous impact. And for our for us on the clinical research side as well.

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Well, sounds exciting and we will, as they say, be watching that space very closely. Lu, thanks again for being with us. If someone wants to get in touch with you or learn more about your work or what Learning Health Network does. Is there a way they can do that?

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Absolutely. You know, we welcome talking to any provider or organization that has EMR data to contribute. If you can contribute our data or health data in exchange for success, we want to talk to you. And this is regardless of whether you are an Oracle customer or not, today our RWD is EMR agnostic. We have data from at least 18 different health records and it's not exclusive.

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So you can join multiple networks, but join ours as well. And you can reach out to us at Learning Health Network underscore at Oracle dot com.

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Great Well if you are interested in how Oracle can simplify and accelerate your life sciences research, we invite you to check out Oracle dot com slash life dash sciences. Also be sure to subscribe to the show because there's more great insight and episodes ahead and join us next time on Research in Action.