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Real-world data (RWD) is having a significant impact on biotech, pharma, and healthcare. Along with giving additional insight into clinical trials that will help to improve patient outcomes, McKinsey estimates that, over the next three to five years, an average top-20 pharmaceutical company could unlock more than $300 million a year by adopting Real-World Evidence (RWE) across its value chain. But what are the challenges, opportunities, and limitations of using RWD? Our podcast with Lana Denysyk, Head of RWD Assets at Novo Nordisk, takes a look.

Guest Profile

Lana’s interest in real-world data took hold while working in quality improvement at a hospital in New York after graduating from Columbia with a Master’s in Public Health. From there, she held several roles focused on real-world data, working at a small life sciences consulting firm and for bigger players IQIVA and BMS before joining Novo Nordisk. She was the first to hold the role of real-world data steward, building up the role to establish the real-world data assets department. In the podcast, Lana is sharing her own views on real-world data, not those of Novo Nordisk.

The Highlights

The FDA defines RWD as “data regarding the usage, or the potential benefits or risks, of a drug derived from sources other than traditional clinical trials.” In the episode, Lana gives us a close-up look at real-world data – what it is, the issues involved in using it, and how it can be leveraged across healthcare and pharma. Here are the highlights:

Where RWD Fits in the Data Ecosystem (6:11): Although Lana sees huge value in RWD as a real-world data specialist, she also acknowledges that it is not always appropriate for all evidence needs. There is a multitude of types of data – clinical trial data, historical clinical trial data, real-world data used for development, real-world data used for commercial purposes, and early research data like mouse models. Therefore, it is incredibly valuable to have a global perspective of the entire data ecosystem. It allows you to question what you need to research and which type of data is best suited to providing an answer because not every type of data is suited to every research question.

RWD in Action (7:48): We asked Lana to give some examples where RWD can help specific stakeholder groups. She spoke about how RWD can assist with expediting clinical trial planning. It can be used to identify potential candidates for clinical trials, pinpointing their locations and the hospitals they use to speed up the process of recruiting patients. RWD is also valuable from a regulatory perspective once a drug is on the market. This data can be used to monitor patients as part of ongoing safety studies.

How to Approach an RWD Strategy (22:23): Lana talks about the need for three things when defining an RWD strategy. Firstly, do you have the right data? What do you currently have, what is missing, and is there a way to access the data you need? Secondly, do you have the right internal resources? Can your employees analyze and work with the data effectively? And finally, does your broader team understand what can be achieved with RWD and its limitations? With all these aspects in place, those in the pharma and healthcare sectors are in a position to leverage RWD effectively.

The Need for Granularity (26:06): With RWD, there is a need to really drill into the details of the data points you need. It's not enough to have a research question and say this type of data is generally useful. You need to be much more granular. This means defining specific variables and how many measurements per patient you need to make sure that that data is relevant and useful.

“Nothing but Challenges” (30:17): When asked what biotech or pharma needs to look out for when building RWD capabilities and what challenges they face, Lana’s response was, “There’s nothing but challenges.” She continues that pharma companies need to be engaging with RWD in the current climate, but with the pitfalls and challenges involved with utilizing RWD, it is crucial to have the right people in place. This is across the board from an experienced legal team, a strong procurement team, an effective IT team working with your researchers, and building cross-department relationships. This helps to build trust in the processes and the data, which is essential for an RWD strategy to be effective.

Further Reading: As is the Data in Biotech tradition, we asked Lana to recommend books and resources to our listeners interested in getting further insight. Rather than any particular books, Lana recommended attending conferences where real-world data and real-world evidence are a focus, speaking with vendors, and keeping up to date with research.

Continuing the Conversation

The nature of healthcare means that a cautious approach is required when looking to integrate RWD. The need to protect patient privacy, differential regulatory regimes, and access to data put limits on the rate at which RWD can be used to make an impact. Yet there has been an explosion in the quantity of RWD vendors and datasets available over the past five years, and the global RWD market size is projected to grow from US$ 1.59 billion in 2023 to US$ 4.07 billion by 2030, at a compound annual growth rate (CAGR) of 14.4%. The FDA’s encouragement to utilize RWD in clinical trials, depending on the circumstances, means that a diverse marketplace of buyers and sellers is emerging for RWD.

This diverse marketplace presents various challenges for biotech RWD consumers that Lana highlighted in her episode, with the most important challenges being:

  1. RWD Acquisition: How to acquire the right RWD assets for your company’s research problem(s) now and in the future. Data acquisition is fundamentally about establishing a rubric for evaluation and an approval process that brings all of the relevant stakeholders to the table. 

  2. RWD Integration: How to integrate RWD assets in a way that enables researchers to discover, utilize, and analyze the datasets they need. For maximum flexibility, this probably looks like a data lake architecture,  where data is organized in a way that enables permissions to be allocated by role, each role to find what they need easily, and data lineage to be tracked from raw sources into more refined versions of the same dataset. Usually, a centralized team would be responsible for integrating the datasets and staging them for analysis in the appropriate locations with the appropriate permissions.

  3. RWD Analysis: How to securely push data access and analysis capabilities down into the business so that insights can be gleaned at scale rather than requiring a center of excellence to handle reactive requests. Companies must establish consistent patterns for the analysis and integration of RWD datasets, including the environments where analysis occurs, the outputs from each type of analysis, and boilerplate examples that give researchers what they need to customize appropriately for their use case. This component also involves training researchers on how to access RWD in their environment and how to use the tools at their disposal.

Generally speaking, more mature organizations will experience more challenges in (3), while organizations just starting their RWD journey will need to spend time and effort formalizing processes for (1) and (2). Regardless of where you are in your RWD journey, CorrDyn can help improve the methods your company uses to make the most of RWD in your operations.

If you're interested in discovering how your organization can unlock the value of data and maximize its potential,  get in touch with CorrDyn for a free SWOT analysis.

Want to listen to the full podcast? Listen here: 

Ross Katz
Post by Ross Katz
February 13, 2024