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The use of real-world data (RWD) has boomed during the pandemic, but the pharmaceutical industry has only scratched the surface in terms of tapping its potential.
The use of such data, however, is set to grow, given the FDA’s recent release of new guidance concerning the use of electronic health records and medical claims data for regulatory decision-making for drugs and biologics.
Two years ago, some people in the industry questioned “whether RWD would have any application in the world of clinical development,” said Jeff Elton, CEO of ConcertAI. Those days are over. It is now clear that RWD is important, given the FDA guidance and the surge in use in RWD during the pandemic.
The level of rigor concerning real-world data is set to grow, given FDA’s feedback concerning the use of such data in clinical trial designs, trial interpretation. “The expectations for RWD are going to be higher, and methodologies are going to improve substantially,” Elton predicted.
However, making the most of real-world data requires a rethinking of research as an activity that occurs in labs or clinics. With RWD, it is possible to think of research as a continuum that precedes the official start date of a clinical trial while continuing after it ends.
Doing so requires identifying the available data sources, including electronic medical records (EMRs), medical claims data, radiological imaging, and independent laboratory data. Sometimes, the process requires integrating a variety of data sources, including data from multiple EMRs.
“You can have multiple clinical data types that may require different analyses,” Elton said.
While EMRs can contain uniquely valuable data, they can have both structured and unstructured data. The former involves machine-readable fields while the latter might consist of notes from nurses and physicians, including radiologists interpreting, say, the size of a tumor nodule.
A business rules engine (BRE) can help sift through the noise by using predefined logic. “But the business rules can also change the value of variables, depending on how you wrote the rules,” Elton said.
Therefore, it is vital to consider those business rules in study design to ensure data accuracy and transparency. “You make sure the rules have clinical meaning so that if a clinician read your rules, they’d say, ‘Yes, that’s how we look at the medical record,'” Elton said.
In oncology, for instance, such unstructured data can contain information related to adverse events, intermediate measures of response or cancer progression.
The recent FDA guidance provides feedback on creating designs of studies and approaches to ensure a high degree of rigor and ethics when working with RWD.
“The most important thing at the end of the day is, often, to let the question determine the data,” Elton said. “Don’t just say, ‘Here’s the data. I hope it does okay against this question of interest.'”
Filed Under: clinical trials, Drug Discovery
