Modern by (Trial) Design: Shedding Legacy EDC Systems to Gain Clinical Capacity

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 organizations (CROs’) clinical capacity. Data managers have to navigate the manual workarounds and custom programming required by legacy systems, which cause bottlenecks. Organizations 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 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.

Meanwhile, sponsors and CROs that minimize custom programming are accelerating study design and build. Lotus Clinical Research, a CRO specializing in supporting novel drug development, not only reduced study startup time by 67% with Veeva Vault CDMS but can now implement changes to the EDC nine times faster than before.

Manual workarounds in legacy EDC cost valuable time during study updates. When studies change, teams spend their time trying to prevent custom functions from breaking down. For example, as Purpose Life Sciences (PLS) prepared to conduct an outpatient COVID-19 trial with 14,000 patients and 12 interventions, it realized the volume of manual workarounds required would make it impossible to secure clinical insights in a timely manner. Michael Zimmerman, AI infrastructure executive at PLS, recalls: “We encountered multiple inefficiencies and had an inability to move fast.”

A more effective approach uses pre-defined variables and functionality within the EDC system to replace custom functions. Study teams can then create rules to automate tasks, complete edit checks, and generate treatment cycles, visits, and forms while avoiding custom programming entirely. This makes the data manager’s job easier and boosts productivity. Once PLS adopted a rules engine to replace custom functions, it could support studies with multiple amendments and drugs across geographies with less manual work and reduced testing (and re-testing) of study elements.

Early protocol changes can delay database builds and the study going live; post-production changes are equally stressful. Although amendments are inevitable, eliminating downtime mitigates their impact. This way, companies can continue to make protocol changes mid-study without risking the trial’s overall timeframe. For example, biotech company Cara Therapeutics, which advances therapies for those experiencing chronic pruritus, made 11 post-production changes with zero downtime or migrations.

Testing is another key driver of delays in study builds. This is most evident in complex branching trials when teams must validate hundreds of possibilities before the launch. However, sponsors can get studies up and running faster by taking a risk-based approach that identifies pre-approved trial elements and removes them from testing (for example, Vertex reduced release times through risk-based testing). Similarly, if teams can complete live, dynamic design reviews of the study in its proper environment, they will have richer conversations and feedback — and make better decisions than when they collaborate via email or spreadsheets.

Business benefits of modern EDC

Agile design can support a rapid evolution in trial capacity and embed new ways of working.

Global CRO Fortrea needed to streamline decision-making, eliminate downtime, and speed deployment of amendments. The company improved responsiveness and eliminated pain points by unifying its clinical foundations, spanning randomization trial supply and management (RTSM), quality management software (QMS), clinical trial management software (CTMS), and electronic Trial Master File (eTMF). It drove the new approach by seeking feedback from staff and sponsors and dedicating enough training time for study team members and experts in global offices.

Successful change management also requires close alignment between technology partners and study teams, who can provide input to future technology development. Fortrea achieved this by clearly communicating where issues might arise to build trust and understanding up front. The company acknowledged that there would be some hurdles to implementation while ensuring user input so that the first studies to go live had the best chance of success.

Now that multiple teams have been trained and use the same platform, Fortrea has minimized programming and time spent on manual-intensive activities, including reducing its use of unique forms by 20-30%. Jerry Yarem, vice president of data management at Fortrea, observes: “That increased efficiency is because the system design allows us to focus on quality.”

Data foundation for decision confidence

There is work ahead to bring the full benefits of modern EDC to trial design. First, by introducing standards for more complex studies, including those involving electronic health and medical records. We need to increase automation in study design — and are making advances in this area — to get trials up and running faster. Most importantly, we can tackle inefficiencies in the non-EDC dataflow, where integrated data reconciliation and cleaning are critical.

A modern approach to clinical data will be a catalyst for more productive trials by helping sponsors and CROs to accelerate their study builds and make clinical decisions with confidence. With a robust data management foundation, teams can focus on science, and move faster to bring new innovations to patients — even for the most complex studies.

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