Blog

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

In the hottest therapeutic areas today, it can feel like everyone is looking at the same recruitment map. GLP-1 and obesity. CNS and psychiatry. Dermatology. Vaccines. Rare diseases in concentrated geographies. Sponsors design different protocols, contract with different CROs, choose different sites—and yet, in practice, they’re often competing for the same limited patient pools at

Clinical trials have never been better measured — and never more vulnerable to the risks that metrics don’t capture. Sponsors are excellent at tracking what happened: enrollment velocity, screen-failure rates, protocol deviations, data queries, adverse events, and time to database lock. These are essential operational and quality signals. But as therapeutic areas become more competitive