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EDC’s Evolution: What Will the Next Phase Bring?

Richard Young
Veeva Systems

Agile approaches and better data integration will be key to improving time to market and patient access to new therapies.

Richard Young, VP of Strategy, Vault CDMS, Veeva Systems

Over the last 25 years, I have made several attempts to predict the future of data management. In truth, clinical trials have transformed in many directions — not least of which, away from traditional reliance on reams of paper. In the 1970s, specialists in the industry began to consider how computer technology could improve data recording and management. After experimenting with approaches that used mainframe computers and PCs, the industry began to work with remote data capture, which became Electronic Data Capture (EDC) technology. By 2004, leading pharma companies and CROs had adopted it. When I spoke to the ACDM around that time, I projected accelerated adoption of technology, new trial design methodologies, and advances in process and delivery driven by automation. Some of that has already been, at least partially, achieved.

Since then, the EDC has become the key entry point into the CDMS and other clinical data systems. It has made many aspects of trial data management easier and more efficient and has cut weeks off the time it takes to get patient data into the clinical database. However, the technology has not been able to keep up with recent changes in trial design. Many EDCs were designed at a time when sponsors used one set of data per trial. Now, most sponsors routinely use three or four data sets per trial, and traditional EDCs are often stretched to fit situations they were not designed for.

To accommodate today’s complex protocols and the need for more — and more diverse — data from different sources, custom functions and external programs are required to enable the EDC to handle steps such as sending emails or running edit checks. These workarounds are expensive and require specialized programmers, increasing the time required for study builds. The result is missed milestones, delaying patient access to potentially life saving therapies and costing sponsors millions of dollars per day.

In addition, there is a need to unify clinical data, which comes in different forms from a wide array of sources. EDCs used to hold most of the data required for each clinical trial. Today, the EDC only holds roughly 25% of that data for most trials. The remaining 75%—third-party data from hospitals, labs, visiting nurses, and other sources—is outside the EDC and disconnected from other trial data. The painstaking effort required to aggregate, reconcile, and clean this data adds cost and time, and prevents data from being made available to clinical teams in anything close to real time.

Agile approaches and better data integration

The use of modern agile programming techniques has made some EDCs more flexible, cutting out expensive programming requirements and repeated rounds of back-and-forth review and amendment. Agile approaches formalize rules for designing trials and amending designs and include standards and libraries of templates that streamline processes and prevent errors. As a result, agile EDCs allow users to turn trial protocols into specs; to standardize and re-use content from previous similar trials; and to adopt exception-based protocol review. By comparing active and previous versions of a protocol amendment, only those sections that have changed — the exceptions — need to be reviewed and acted upon. Several pharma companies and CROs have found that agile EDCs reduce their setup times by 50%.

Meanwhile, to connect EDC and other clinical data more effectively, companies are developing data workbench tools to hold, clean, and regularly update diverse clinical trial data in one place, making it available to the right users at the right time. These tools promise to reduce the need for manual data cleaning and reconciliation and improve regulatory compliance and time to market.

EDC technology, the role it plays, and the environment in which it operates are sure to change in the future. The next stage of evolution will focus on increasing the flexibility and connection between the EDC and other clinical trial data. Both are crucial to improving clinical trial timelines and quality.

It would be exciting to sit in a room with top industry leaders today and set out a fresh vision for 2030. Predictions like this fuel our thinking and create new innovations every day.

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