Why Payers Are Shifting Away from Licensing Out RWD

The pharma and biotech industries run on data, especially on the R&D side. Data is one of the most valuable assets, but it can also cause some of the biggest headaches.

Due to the wide array of sources and collection methods, data sets may not always be easily compatible with others. Adding to the complications, big companies generally view data as proprietary and aren’t comfortable sharing it.

Things are only getting more complex with the rising acceptance of real-world data (RWD). In April of this year, David Lazerson and Fabio Lievano wrote for Pharmaceutical Executive about the rising importance of RWD. In their piece, they said, “FDA is changing the rules. Marking a fundamental shift in how we define acceptable clinical proof, it has indicated that one well-controlled trial, supported by confirmatory evidence––in particular, real-world evidence––will become the default for drug approvals. Many are applauding this as a sign of more regulatory flexibility.”

Pharmaceutical Executive recently spoke with Mark Cziraky, president of Carelon Research. The company just signed a deal with Manifold that allows life sciences researchers and data analysts to analyze Carelon Real World Data (RWD) alongside third-party datasets.

The deal is part of an ever evolving landscape in which data and how its controlled is becoming one of the most important tasks of any company. During his conversation, Cziraky discussed shifts in how RWD is perceived and how companies are viewing it as a different kind of asset.

Pharmaceutical Executive: Why are payers shifting away from licensing out RWD?
Mark Cziraky: The core issues always come back to privacy, governance, and ensuring appropriate use of the data. We want to be responsible stewards of the data we work with — whether that's on our services side, where we operate within our own environments, or when we're making data available in other models, typically in de-identified form through flat file licenses and other products.

What a governed model provides is confidence that data is being handled appropriately. And as technology has advanced, it's now possible to bring compute to the data — rather than moving data out to different environments — which reduces the risk that comes with data transfer and storage.

It also significantly reduces contracting complexity. When life sciences companies and other data partners want to bring data assets together, they typically have to execute multiple contracts, work through substantial data cleaning, and manage complex integration on their end. That process is time-consuming and, at times, quite challenging.

A trusted research environment with pre-governed, pre-integrated data assets addresses all of that. By making those data assets more ready to use, we can meaningfully increase the throughput with which real-world evidence can be generated.