As trials become increasingly complex, companies share how leaving behind legacy EDC systems can save thousands of hours during study build and testing.
In today’s context of complex trials, traditional electronic data capture (EDC) systems are not flexible enough to address frequent protocol amendments. They hinder study teams by creating hidden data management issues that can affect sponsors’ and contract research organisations (CROs) clinical capacity. Data managers have to navigate the manual workarounds and custom programming required by legacy systems, which cause bottlenecks. Organisations already grappling with the exponentially increasing volume of data (through new sources like wearables, ePRO, genetic biomarkers, etc.,) lack a single source of truth now that most critical trial data can come from outside EDC. Delayed clinical insights and the increased cost of executing studies invariably feed through to trial outcomes.
Leading sponsors and CROs can avoid common pitfalls by ending their reliance on custom programming and embedding rules and dynamics to reduce study build times. Others have introduced risk-based user acceptance testing (UAT) rather than attempting to validate hundreds of possibilities before launching a study. Given that protocol changes are inevitable (and sometimes even preferable) in complex studies, it is better to eliminate downtime and/or migrations so that amendments become a positive change agent rather than something to be avoided at all costs.
Since companies started to adopt EDC decades ago, the lag period between data being recorded during a patient’s visit and appearing in the data management workstream has decreased only marginally, from eight to six weeks. The burden of data entry shifted from biopharma sponsors to sites, which fragmented the data management model without addressing core challenges. With new systems, we are getting closer to complete and concurrent data review.
Enhancing Clinical Capacity
Most traditional EDC systems need custom programming to achieve desired study designs, partly because the study architecture isn’t flexible to nuanced trial requirements. The more custom programming is needed, the greater the investment to build, test, and maintain these programs.
Custom functions are particularly challenging for biotech companies because skilled resources are at a premium and needed elsewhere in the trial. As a result, these small and midsize companies often partner with CROs: Deepak Mahadevaiah, senior director for clinical data sciences at Agenus, points out, “We don’t have the skillset in-house and are dependent on a technology partner.” The costs can rack up quickly. In a recent study (not undertaken for Agenus), one CRO spent over 1,100 hours building and testing over 200 custom functions.
