IRT & EDC: Achieving a Unified Experience

In rare disease drug development where no registry or natural history dataset exists, real-world evidence quality depends on treating evidence engineering as a design-stage decision, defining intended regulatory use and standardizing endpoints, harmonization, and data provenance upfront rather than reconciling gaps after collection.

Most AI pilots in drug development fail not from poor technology but from lack of strategic prioritization, organizational readiness, integrated data infrastructure, and disciplined governance, making success dependent on business discipline rather than technical capability.

Clinical trials have spent decades perfecting data capture, but the execution layer underneath it, including decision workflows, data collection design, and lab connectivity, remains fragmented in ways that limit what AI can realistically deliver and that quietly compromise the scientific validity of the data itself.

In this Q&A, John Worden, chief commercial officer at Javara, discusses why clinical trial recruitment has remained stubbornly one-size-fits-all, how AI can identify patients at scale without displacing the clinician relationships that drive trust, and what care-integrated research engagement needs to look like for health systems, sponsors, and sites.

Participant engagement depends more on clarity and understanding than on gamification or incentives, requiring engagement embedded into trial workflows as a core design component rather than a peripheral consideration