Complexities in Data Management

ACT: What are some of the biggest challenges you are currently seeing with data management in clinical trials?

Lacroix: Since I've been in industry for over 20 years, the complexity of protocols which we continually hear; you talk to people in industry—we're going from one indication, one study drug, to multiple indications, multiple study drugs. The majority of the trials that we're seeing at eClinical are platform trials, basket trials, and all of these master protocols and adaptive trials that are extremely complex. In addition to that, we're seeing a lot of different data types and data sources that we've never seen before, and they're just continuing to grow. Coming from historical trials where we had EDC (electronic data capture); it’s a very site-centric data collection. We still see EDC, but we're seeing a lot of other data acquisition tools that are adding complexity to that diversity of data sources and data types. The volume as well; the volume of data is very large. We're processing extremely large data sets and managing all of those data flows and for data managers, it's very different. We're trying to coordinate, manage, and work with multiple stakeholders across study teams, so people often don't think about that part of it in that not only are the protocols getting more complex, but it introduces new players, new stakeholders, more decision-makers at the table that we have to coordinate and manage. Of course, with all of this, there's continual pressure to do it faster. We want data insight sooner—in that reduction in cycle time and cost and risk reduction—that pressure is just continuing to grow in the face of all of this complexity.