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Duplicate subjects in clinical trials and professional research subjects do not operate in isolation. They move across studies, sponsors, CROs, therapeutic areas, and geographies—often undetected when safeguards are fragmented or limited to individual sites. This is precisely why point solutions fail and why a networked research subject database becomes exponentially more powerful as adoption grows.

Verified Clinical Trials (VCT) will be attending SCOPE Summit 2026, where leaders from across the industry convene to advance the future of Clinical Trials. At SCOPE, our team will be reconnecting with many of our long-standing clients, colleagues, and friends, while also meeting new sponsors, CROs, and research partners. These conversations are essential as the

Duplicate subjects in clinical trials and professional patients are no longer rare anomalies—they are structural risks in modern clinical research. As trials scale globally and indications become increasingly crowded, these issues quietly undermine data integrity, inflate costs, and delay life-saving therapies. A recent investigative article in Science brings this problem into sharp focus:👉 Alzheimer’s drug

When Verified Clinical Trials began, there wasn’t a crowd waiting for us — but there were investigative sites, Phase 1 units, and forward-thinking sponsors quietly rooting for someone to build critical mass around a problem they knew was real. They were seeing the same patterns over and over again: What was missing wasn’t awareness —

When Verified Clinical Trials (VCT) first launched, the idea was simple: What if sponsors and sites could share a single, privacy-protective way to see when a research participant was already in another study? At the time, preventing duplicate subjects in clinical trials, catching professional patients, and using a shared research subject database / clinical trial

Verified Clinical Trials: The global research subject database Most discussions about clinical trial economics start with big, visible levers: Those all matter. But there’s another lever that’s usually underplayed in financial models—precisely because it’s quiet and hard to see: The quality of who you actually randomize. Not just how many participants you enroll, but how