From point-of-care sample collection to real-world data curation, wearable device integration, and continuous review infrastructure, data quality is not a single problem in clinical trials—it is a layered challenge that manifests differently at every stage of development and demands a different set of solutions at each one.
Clinical trial modernization is moving fast, but the foundational problem has not changed: data quality remains the ceiling on everything else the industry is trying to build. Artificial intelligence, real-time oversight, and real-world evidence all depend on data that is accurate, complete, and trustworthy from the moment it is collected. That condition is still far from guaranteed across most of the clinical trial ecosystem.
