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Overview
For biotech and pharma companies, a fundamental challenge lies in bridging the gap between highly controlled clinical trial data and the complex realities of patient health experiences outside of trial settings. Without a structured approach to real-world data (RWD) acquisition and management, organizations risk making strategic decisions based on incomplete information, duplicating efforts, and incurring unnecessary costs.
In this episode, host Ross Katz speaks with Lana Denysyk, Head of RWD Assets at Novo Nordisk, who brings direct experience managing the complexities of integrating RWD into a major pharmaceutical company. She discusses how RWD provides a critical window into patient outcomes, drug adherence, and safety once treatments are in widespread use, expanding its role from clinical trial planning to post-market regulatory compliance.
The conversation covers RWD’s evolving definition, its varied applications across development and commercial teams, the crucial role of a centralized RWD function, and the strategic considerations for building reliable RWD capabilities. Denysyk highlights the impact of data quality, privacy regulations like HIPAA and GDPR, and emerging data types on future health equity research.
Key Takeaways
Real-World Data Uncovers Actual Patient Outcomes, Beyond Clinical Trial Limitations.
Clinical trials operate under controlled conditions to isolate treatment effects. Real-world data (RWD) provides visibility into how treatments perform in diverse patient populations living complex lives, revealing critical insights on medication adherence and varied outcomes in patients with comorbid conditions not typically included in trials.
A Centralized Real-World Data Team Prevents Costly Duplication and Research Silos.
Without a dedicated team focused solely on RWD acquisition, contracting, and management, organizations often duplicate data licenses or conduct redundant studies across different geographies. A central RWD function ensures coordinated data strategy, optimizes resource allocation, and allows researchers to focus on analysis rather than data procurement logistics.
Data Quality is Context-Dependent; Research Questions Must Specify Granular Data Needs.
The usefulness of a dataset is not universal; its quality must be assessed against specific research questions. Simply acquiring broad data is insufficient; researchers need to define precise variables, data points, and measurement frequencies (e.g., three lab values over time for each patient) to ensure the data can truly answer their hypotheses. Vague requirements lead to wasted investment in irrelevant data.
Patient-Reported Outcomes and Social Determinants of Health Data Inform Future Health Equity Insights.
While traditional claims and EMR data are established, emerging RWD types offer deeper insights. Patient-reported outcomes (from wearables, health apps) provide the patient’s perspective, while social determinants of health data reveal environmental impacts on health outcomes, enabling a more complete approach to health equity research and intervention development.
Related: CorrDyn helps organizations with data assessment and building managed data teams that improve data quality. Learn more about how biotech manufacturers gain data value.
Full Transcript
Jason: Welcome to Data in Biotech, a podcast from CorrDyn where we explore how companies leverage data to drive innovation in life sciences. Every two weeks we sit down with an expert from the world of biotechnology to understand how they’re using data science to solve technical challenges, streamline operations, and further innovation in their business. This week we’re joined by Lana Denysyk, head of real-world data assets at Novo Nordisk. During the interview, Lana shares how real-world data is used in biotech, diving into how it can help clinicians understand patient experiences outside of clinical trials. Lana also discusses the challenges in acquiring and utilizing real-world data, the importance of having a centralized team to manage it, and emerging data types like patient-reported outcomes and health equity data. Here we go.
Ross Katz: Lana Denysyk, welcome to the Data in Biotech podcast.
Lana Denysyk: Thanks so much for having me.
Ross Katz: To kick us off, would you mind giving us a brief introduction to your background?
Lana Denysyk: Absolutely. Before I start, I want to say that I am an employee of Novo Nordisk, but I’m only discussing my personal opinions and reflections. My career to date: I earned my master’s degree in public health at Columbia University. After that, I started working in quality improvement at a hospital in New York. That was where my interest in real-world data really grew because I saw how impactful it could be when you’re looking at patient outcomes in the real world — seeing how the application of care to patients, tracking it, understanding it, can really improve outcomes quite directly. From there I moved to a small life sciences consulting firm where I learned a lot more about pharmaceutical companies and how they use real-world data. From there, I went to IQVIA, which is quite well known in the space of real-world data. I worked on the real-world solutions team, working with pharma companies on their strategy of how they use real-world data, what sources they might use, how to best approach that. From there I worked at BMS as an associate director of real-world strategy, and from there I moved to Novo Nordisk and I was the first one in the role of real-world data steward. I built up the role and I now head up a department called Real-World Data Assets at Novo.
Ross Katz: What does your role entail at Novo Nordisk?
Lana Denysyk: Again, this department is Real-World Data Assets. We manage secondary-use real-world data licenses, basically ensuring that our research teams have accessible and usable real-world data. And I also have a strategic focus at the corporate level to understand how we can better leverage real-world data and real-world evidence across the organization.
Ross Katz: As you reflect on your time doing quality improvement at a hospital at the beginning of your career, do you think differently about those days based on all of the knowledge you’ve acquired from being in the real-world data ecosystem over the last several years?
Lana Denysyk: Absolutely. I still reflect on the fact that the hospital was still using paper charts that were scanned in. Analyzing that data was incredibly difficult. Understanding how technology really helps to analyze data — how input of data from a physician’s standpoint, from a hospital standpoint, having the structure to input this data is so important — because you can have all the data in the world. This is a problem we have with big data in general. If you can’t query it easily, if you can’t clean it easily, if you can’t pull out what you need easily, then it’s useless.
Ross Katz: So you spent this early part of your career beating your head up against the challenges of having all the data you needed to answer critical questions in a hospital environment, and now you’ve spent the next part of your career liberating the data and making it available to all of these people who need to answer the critical questions to advance the life sciences. Am I thinking about that right?
Lana Denysyk: Yeah, exactly. That’s really well put.
Ross Katz: When we talk about real-world data, it’s worth level setting about what we’re talking about. Since you’re living in it day to day, can you give us an orientation to what we mean when we talk about real-world data? What’s its function and how do biotech organizations use it?
Lana Denysyk: Absolutely. There are so many different ways to define real-world data, but the definition I use is basically all data around a patient’s health or healthcare that doesn’t come from a clinical trial. It can be so broad. It can be your claims data when you have your insurance pay for your care. It can be your electronic medical record data — we call that EMR data — and that’s when you go to your doctor and they put in notes in the chart. It can be your Apple Watch or your Fitbit, an app that you use to track your health or your mood. It can be your lab test, your MRI scans, and your genomic data if you do, for example, 23andMe. All of that can be real-world data. The function of real-world data is to show what’s actually happening with the patient while they’re living their lives. In a clinical trial we have incredibly controlled circumstances under which patients receive care, take medication, have tests conducted, and it has to be that way so that we can understand if the treatment has efficacy. But patients aren’t always living under ideal circumstances. Life is pretty messy. Real-world data helps us understand: with patients who have more comorbid conditions than are reflected in a clinical trial, are they still having the same outcomes? Are patients having trouble adhering to their medications? In a clinical trial it’s much easier to track adherence, but in the real world it’s actually one of our hardest issues to tackle because you can see if a patient picked up a drug, you can see if insurance paid for it, but you can’t know if they actually took it. You can make assumptions, but you just never know. And to see what their outcomes are in the real world — if we assume they’re taking their medications, they’re having their tests done, they’re seeing their doctors, is that showing improved outcomes when things are under less ideal circumstances?
Ross Katz: It seems like a lot of the processes that biotech companies undergo in bringing therapeutic solutions to market are in very controlled environments where you’re trying to make sure that all of the other variables and factors that might influence the outcome you’re measuring are controlled for. But then, when you bring them to market, patients are using them every day in circumstances that are much more complex. The real-world data is that window into what happens when these controlled circumstances we’ve designed in the lab to measure the outcome we care about meet the circumstances in which people live their everyday lives.
Lana Denysyk: Exactly. That’s really well put.
Ross Katz: In that context, since you have exposure to all of the data assets that a biotech organization gathers — from R&D through clinical trials and commercialization — can you help us understand where real-world data fits into that broader data ecosystem and how it gets integrated in?
Lana Denysyk: Real-world data is just one component of a broad ecosystem of data. As you mentioned, I don’t have visibility into every type of data, but we have clinical trial data, historic clinical trial data, real-world data used for development, other types of real-world data used for commercial purposes, early research data, things like mouse models. There are so many types of data we can work with. Real-world data isn’t always appropriate for all of your evidence needs. I think it’s incredibly valuable to have an overview of your entire data ecosystem in an organization and be able to say: here are our evidence needs, here’s what we need to answer, what type of data is best suited to that? Every type of data will not be best suited to every type of research question. You need to be able to distinguish where something should fit. Having that overview helps you because otherwise it can become siloed and you might not be leveraging the best data for your evidence in the most effective way.
Ross Katz: The way you know how real-world data fits into that broader data ecosystem is based on the questions that the organization is asking and the situations in which real-world data can be brought to bear as the best data asset to assist with answering that question.
Lana Denysyk: Exactly. In general, the data should never drive the research question — the research question should always drive what data helps you answer that.
Ross Katz: Can you give me some examples of the types of stakeholder groups that have research questions where real-world data is brought to bear?
Lana Denysyk: Stakeholders that use real-world data could be members of the development team who are helping bring a drug to market. The use of real-world data is inching up in the timeline of drug development. It can start from helping plan a clinical trial to understand where the patients are so that we can recruit them. Real-world data can help you find what hospital systems they are in, and you can contact that hospital system and know that you can recruit there. It can come to clinical trial planning as well — there are tools that exist where you have a funnel showing: if I use these inclusion-exclusion criteria, I will filter out way too many patients and won’t be able to recruit for this clinical trial. Let me see if I can edit these inclusion-exclusion criteria and then have more success in recruiting and finding our patient population. There are other departments where they need to fulfill regulatory requirements, for example from regulatory bodies who say you have to show us a safety study. They would be using real-world data to show the safety of the drug once it’s on the market. We also have commercial colleagues who use it for discussions with payers or regulators as well.
Ross Katz: The place where I don’t see real-world data being primarily used is on the R&D side — does this particular therapeutic approach or drug work? That’s prior to the real-world data conversations. But once we have an understanding of the biology underlying a therapy and are beginning to think about bringing the drug into a clinical trial environment, that’s where the real-world data starts to become valuable. Am I thinking about that right?
Lana Denysyk: I think that’s where it’s historically been, but all of pharma is moving to using real-world data to see if the drug is working. A lot of pharma companies are doing things like innovative trial design where you leverage real-world data as part of your clinical trial. That could be something like having a historical control arm where rather than recruiting patients as a control arm, you can develop one in retrospective data and not need to recruit those patients. You can also leverage real-world data as part of the active arm of the trial and have patients input information — or have their doctors input their information and pull from their EMR. That saves patients a lot of time and burden on driving to the site and having these additional tests conducted if they’re also receiving it as their point of care.
Ross Katz: From the patient perspective, that’s integrating the clinical trial into the existing care processes they’re already undergoing rather than asking them to do an additional thing, but still providing the real-world data that the pharmaceutical company needs in order to answer the questions that are critical to the success of the trial.
Lana Denysyk: Exactly. And it always depends on the clinical trial of how you can do that and still abiding by the clinical trial regulations and processes. But we see that being integrated more and more across all pharma companies.
Ross Katz: You’ve got all of these stakeholder groups across the organization that want real-world data to inform their processes. I’m assuming there are resource limitations or a prioritization exercise that needs to happen to decide who gets what real-world data and in what quantity and from what vendors. Can you walk us through how you think about that process and how you think about evaluating or scoping a request for real-world data?
Lana Denysyk: Absolutely. I’ll answer that question by answering another question first. In general, there are so many stakeholders across all life sciences companies who want to use real-world data to answer all their questions. The first issue I think we come across is: what are the limitations of real-world data? What can’t you do with the data? Because there can be a lot of expectations of “this will be easy, just buy this data, answer this question and we’re done.” But it’s really not a straightforward process to even answer a simple question. To give an example, let’s say we just want to know how many diabetes patients are in a given geographical area. How are you defining these patients? They don’t all have an ICD code of a diagnosis. And if we know they don’t have an ICD code, how do we pick up the rest of that population? You might be missing the majority of the population in a certain claims data set or EMR data set. So what else can we do? How else can we find them? Let’s start looking at their lab values — that could be a way to find a patient. Are their lab values at a level that shows they likely have diabetes? But how many patients have labs measured regularly? How many have access to healthcare on a regular basis and have that input into their chart that we can see? There are claims data sources where we can see a lab test was billed for, but we can’t see the value of it. So we know they had that done, but was it elevated or was it normal? And when you go to geography, location is typically masked for patient privacy because if we have that deep clinical data from a patient, we can’t have other identifying information like their geography or where they’re located, because it might make them identifiable. There are so many pitfalls in what seems like a simple question — how many diabetes patients are here — it’s really not easy.
So I think that’s the biggest issue across stakeholders in general. Once it’s been determined that real-world data is a relevant data source for a research question, there’s really a balance between should an organization prioritize data sets that are broad and have many therapy areas and have the ability to answer many research questions for many teams, or should they focus on a very specific data set that has exactly what they need but can’t be used for any other purpose?
And I think for an organization, you always need a balance. There are always research questions that do require a really specific data set that captures variables that are critical to the research. But then there also should be a balance of here’s a broad data set that has a lot of information about patient care and patient outcomes, but it might not have these really hard-to-find variables that some studies might need. And from there, you can link or modify or add to the data in certain circumstances.
Ross Katz: What this highlights for me is that the way I framed the request is as a reactive request coming from the stakeholder. The way you’re positioning it is that organizations need to have formative input from real-world data experts like yourself into the strategy of acquiring the data, which requires someone like you to have visibility into the entire scope of research questions you want to be asking of real-world data so that you can deploy the resources you have in the most effective possible way to answer the maximum number of research questions with the best data available.
Lana Denysyk: Exactly. I do think it requires having a central team focused on real-world data so that the people who are focused on research, focused on delivering evidence, can focus on that. Having a team who is only focused on real-world data — how to acquire it, how to manage it — is very useful for an organization so that they’re not operating in silos or having data scientists and researchers figuring out how to work with contracting and legal to get a license through. That shouldn’t sit with them. It requires both having a central team managing real-world data and an organizational approach to assessing evidence: how do we determine what evidence we as an organization need? Without that, you are operating in silos and you might be duplicating data licenses — spending money unnecessarily — or conducting the same study in different geographies. Headquarters might do one thing, an affiliate might do the same exact thing, spending the same amount of money, maybe defining their patients differently because they’re not coordinating together. There’s a huge risk to organizations if they don’t have this central view of evidence and data.
Ross Katz: The role that you and groups of people who own real-world data play is connecting the dots between these different stakeholder groups, facilitating the process of acquiring the right data that meets everyone’s needs while acknowledging the resource constraints and prioritizing as a collective which research questions should take priority from a data acquisition and data strategy perspective.
Lana Denysyk: Of course. Resource constraints are a common issue among pharma companies, especially when it comes to analyzing the data. Ways to manage it are to have more platforms and tools that allow those who are not data scientists to query the data more easily and answer faster questions — so that it’s not requiring a data scientist to get in and really work through a research question if it could be something simple that you just need to know to help answer a question you have in a meeting. It’s not something that will be published scientifically.
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Ross Katz: The real-world data sets you mentioned are diverse, but similar in the way they’re acquired and in how you’re utilizing them to answer questions. What I’m hearing is that it’s better to have standardized processes for managing the data and getting it to analysis readiness as part of a centralized governance of real-world data.
Lana Denysyk: Absolutely. As the industry evolves, as the world evolves, we have so many new types of data. Having a standard process allows for the flexibility that when there is a new data type an organization might have never worked with before, at least they know how to bring it in and shepherd it through the organization and make it available to researchers. Even though it’s a different type of data, it’s still the same process.
Ross Katz: What does a standardized process like that look like? What are some of the considerations you should take into account when putting one in place?
Lana Denysyk: A lot of organizations can benefit from having plans in place for working with procurement and legal, the data protection office, and IT teams to contract for the data and then to land it. What environment is it in within your organization? Is it something that researchers have access to? Do they have their preferred tools and languages to actually analyze the data?
Ross Katz: Should I think about that as a centralized data stack — like a data warehouse or a data lake — and then a set of tools?
Lana Denysyk: I think you could think about it as a data lake, yes.
Ross Katz: So there’s a centralized repository where all of the researchers who need access have access to the right data, but there’s access control around who can access what data based on their permissions and their actual need for gaining access to particular sets of data. You mentioned regulatory issues and how you navigate that. When you think about complying with privacy regulations like GDPR and HIPAA, how does that play into gathering these big real-world data sets together in a place where researchers can access them?
Lana Denysyk: That’s a huge challenge for global companies, which most major pharma companies are. There are so many regulations they need to abide by. Within every geography a company may be working in, they need to ensure they’re abiding by local regulations as well as the patient privacy regulations in the geography where the patient data is. Companies need to be aware of HIPAA, GDPR, and local regulations because some local regulations may go even further with certain data types. It can have a really large impact at the point of contracting and receiving the data, and this all needs to be resolved prior to that so that the company is not put at risk and patient privacy is never compromised.
Ross Katz: We’ve got these different privacy regulations that you have to abide by based on where the patients are coming from and where the companies are located. What I’m hearing you say is that you have to take a maximalist approach to the regulatory requirements the entire organization faces and basically make sure that all of the real-world data you’re acquiring abides by all of the regulatory regimes you’re subject to. Is that right?
Lana Denysyk: Exactly. We never want to put patient privacy at risk and that is of utmost importance. There might be better solutions in the future, but for now that maximalist approach is a good approach for pharma companies to take to ensure that patient privacy is never put at risk.
Ross Katz: By getting exposed to all of these different research questions and all of the data sets that are out there — and staying up to date on the new data sets becoming available and the different offerings vendors have — you have a unique lens into real-world data strategy, looking three to five years into the future at where an organization should be. Can you give us some insight into how you think about constructing a real-world data strategy over that time horizon?
Lana Denysyk: Strategically, pharma companies should be looking at a few different things. First, do you have the right data? Are you able to answer the research questions you currently have with the real-world data you currently have? If not, conduct that gap analysis and see what’s missing, then have a plan to find the data you need. Second, do you have the right resources within the company? Are your employees trained to analyze this data and work with it in the most effective way? Are they learning from each other? Lastly, are people who aren’t data scientists — who aren’t directly in the research at the company — aware of how to use real-world data and the best way to leverage it? I think it’s really important for many people across a pharma company to be at least knowledgeable in what can real-world data do, what can’t it do, and how could it be applied. All of those things are necessary for a strategic lens on how pharma companies can better leverage real-world data in the future.
Ross Katz: Based on all the vendors you’ve interacted with over time, what advice or guidance would you give to real-world data vendors to position their data assets to be most valuable to biotech and pharma companies?
Lana Denysyk: Robust and representative real-world data is really important and I see it as a powerful tool to focus on health equity. What is growing but still in a nascent stage is representative data that reflects patient populations. Vendors should account for how health equity is measured in their data, and how they utilize the findings to improve patient outcomes. For example, is there a way to link those patient outcomes to social determinants of health or other health equity data variables?
Ross Katz: Are there key questions that you ask about the data assets being presented to you that vendors might tend to not be as prepared for as they could be?
Lana Denysyk: I’ll phrase this in a different way. The research drives what our data needs are. When a pharma company is speaking to a vendor, they really need to account for what specific variables are needed for a certain research question. A vendor says they have this variable available for almost all their patients — that sounds very nice. But that pharma company might need at least three measurements of that variable over time to track how that patient is doing, and then that number can drop significantly. My advice to pharma companies is to really get into the granular details of how you’re going to do this research, which specific variables that are typically hard to find do you need, and go after those. That will impact how you can do your research, and you really need to get granular and talk about specific variables and how many measurements per patient you need to make sure that data is relevant for you.
Ross Katz: From the vendor perspective, it’s understanding the research questions that the biotech and pharma companies are asking so they can position their data sets relative to the research question, since that’s the source of all need coming to the vendor. From the biotech and pharma company perspective, what I’m hearing is they need to define their requirements much more specifically. It’s not enough to have a research question and say generally this type of data is useful to answer this type of research question. You need to be specific: ideally we would have these specific data points about every patient in the data set over time, in order to sufficiently answer the research questions for the requirements of the organization.
Lana Denysyk: Exactly. Yeah, I think if a pharma company approaches this as just saying here’s a broad research question, I think this data source might work, I think that’s a really easy way to spend a lot of money on something that’s not useful for you. You really need to be specific about how you will conduct this research to get the most value out of the data that you license.
Ross Katz: It sounds like part of the role of this centralized real-world data organization is teaching the organization to ask the right questions about the data they want to acquire and making sure that research questions are well-formulated and meeting these minimum standards for what information is needed to know what data needs to be acquired.
Lana Denysyk: Exactly. That’s part of this objective approach to assessing the quality of a data source specific to a research question. There’s of course a way to assess quality of data in general, but I don’t think that goes far enough. You can assess: is this data good? Yes, it may be — but then you do have to take it to the next level and ask: is this relevant to the research I’m currently conducting? Both need to be true for this to be a relevant data source.
Ross Katz: In this way, quality is very context specific — a data set that is of high enough quality in one context might not meet the quality standards of another context. As you look toward the future of real-world data, are there any emerging data sets you’re excited to get your hands on or get broader access to? Any new types of questions that you think people are going to be able to ask inside of biotech and pharma companies that they weren’t able to ask before?
Lana Denysyk: In general terms, pharma has typically been working with claims and EMR data sources for quite some time now. There’s still room for growth across many pharma companies to utilize them, but they’re fairly well established in use. Going beyond those to patient-reported outcomes — things like your Fitbit, your Apple Watch, an app that you track your health or mood — understanding the patient’s perspective and their view on their health. As I mentioned, the social determinants of health data: there’s so much that impacts our health that has nothing to do with us. The environment we’re in can really impact our health. Understanding how it impacts our health and how we can then address that to allow for health equity in patient outcomes is really important. I’m excited to see more of that type of data, and other data like leveraging more imaging data or omics data. Those are newer types of data that are becoming available and being leveraged more by pharma companies.
Ross Katz: For anyone in the process of building up real-world data capability at a biotech or pharma organization, are there any challenges you would advise them to watch out for in building up these muscles internally — gathering these data sets together, standardizing processes, getting the capabilities out to the rest of the organization to be able to analyze it?
Lana Denysyk: There’s nothing but challenges in putting this together. It’s necessary for a pharma company to operate in this day and age, but there are so many challenges and pitfalls. My advice would be: get the right lawyers to help you, have the right teams around you — your legal team, your procurement team, your IT team, and your researchers — and really build those relationships so that those around the organization can trust that you are working to get them the data they need and are willing to work with you and have it go through the central team.
Ross Katz: If there’s any mistrust in the system, it’s very possible for waste or an end-around to happen where data gets acquired that shouldn’t be acquired, or it’s not stored in the way it’s supposed to be stored, or there are complaints about the additional layers of process that need to be gone through in order to get the data. People want to move fast, but as an organization you need to move slightly slower in order to get to a better outcome.
Lana Denysyk: Yeah, and I think in general without the trust pharma companies really risk remaining in their silos. That trust is necessary to break those silos down and have it be a much more open collaborative environment.
Ross Katz: For anyone coming up to speed or staying up to speed in the real-world data arena, are there any resources you would point people to — blogs, substacks, books, podcasts — that you think would be helpful in keeping their finger on the pulse of this area?
Lana Denysyk: Attending conferences where real-world data and real-world evidence are highlighted, understanding what research is currently being conducted, talking to the vendors — in particular at these types of conferences — to see what types of new data are being made available for licensing. There’s so much to learn from speaking to the vendors themselves, others who are conducting real-world research. Just ensuring that you’re up on the research, especially in therapy areas of focus for your company. Seeing what types of data sources they’re using: if you see someone publish an article or publish some research around real-world data, look to see what was the data source listed, and if that’s something interesting to you then reach out to that vendor and start talking to them about what they can do for your research questions, what they can make available for you.
Ross Katz: Researchers who have specific research questions and have had to acquire their own data are forced to go through that process of determining what the best data source is that’s available to answer that question. That can be a signal out in the market of the best data sources available for a given research question type, or what the market trends are in terms of the types of data people are acquiring to answer certain types of research questions. That makes a lot of sense. Well, Lana, thank you so much for joining us today. It was a real pleasure to hear more about your world and to learn about the world of real-world data. Looking forward to connecting down the line.
Lana Denysyk: Great. Thank you so much for having me.
Jason: And that’s it for this episode of Data in Biotech. If you enjoyed the episode, please subscribe, rate, or leave a review in your podcast platform of choice. See you next time.
