Trusted Research Environments Are Reshaping Real-World Data Access: Q&A with Mark Cziraky

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.

Click here for the full interview!

Pharmaceutical Executive: What is causing the shift in how the industry perceives RWD?
Mark Cziraky: The traditional model of real-world evidence generation involves bringing data into data environments and leveraging it through various analyses — either independently or by working with services organizations like ours within our own data environment.

What we're seeing now is a growing need for more data, deeper data within specific environments, and the ability to bring multiple data assets together to answer increasingly complex questions. A trusted research environment addresses exactly that. It provides a governed model that can expand access to data, enable integration across different data sources, and ultimately give end users the ability to generate richer, more actionable real-world evidence from a more comprehensive and secure foundation.

PE: Why are payers shifting away from licensing out RWD?
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.

PE: What are the mechanics of analyzing linked data sets?
Cziraky: The analytical process itself is largely the same whether you're working with data you've licensed and brought in-house or data you're accessing within a trusted research environment. What changes significantly is everything that happens before the analysis begins — the process of acquiring the data, bringing it in, and the level of quality and detail you can actually access.

There are meaningful restrictions on what data can be included when it's transferred in de-identified flat files, compared to what's available when data sits within a more governed environment. A trusted research environment can expand both the volume and the richness of data available to researchers, which ultimately leads to better real-world evidence.

Once you bring in the compute and begin the actual research, the work follows standard scientific practice. You identify whether the data can support the evidence you're trying to generate, run preliminary queries to inform study design, and then execute with the same rigor you'd apply in any environment — developing protocols, applying appropriate analytical methodologies, generating outputs, conducting quality assurance, and developing the evidence package.

At the end of the day, whether you're acquiring data in flat files and loading them into a data lake or accessing data within a governed environment, it's all about the evidence you're trying to generate and the insights that come from it. The model is secondary to the outcome.

What we're working toward is another pathway for generating high-quality evidence more efficiently — a pre-governed, pre-integrated environment where access can be controlled and researchers can move faster from question to answer.

PE: What are the research timelines when using best RWD practices?
Cziraky: The answer varies depending on the research question, so it's difficult to answer broadly. What I can say is that timelines are dramatically reduced in the areas that have historically created the most administrative burden — identifying the right datasets, acquiring them, ingesting and cleaning the data, and combining multiple sources. Those steps, which can consume a significant portion of a project's timeline in the traditional model, become much more streamlined in a trusted research environment.

The focus shifts to what actually matters: confirming that the data are appropriate for the question at hand, and then getting into protocol design and execution as quickly as possible. Trusted research environments also allow for pre-approval of specific analytic tools used within those environments — which, as technology continues to evolve, creates additional efficiencies in the analytical process itself.

So yes, there are meaningful reductions in the time it takes to get from question to evidence. But I want to be clear: this is not about cutting corners or reducing rigor. The governance requirements, data protections, and analytical standards remain fully in place. What changes is that the data are more ready for analysis, the appropriate datasets are pre-identified, and the agreements that enable access are already established. The result is a more efficient path to the same high standard of evidence.