Precision Recruitment: Fixing The Future Of Clinical Trials

By Kieran Reals, Erik Moen, Jenna Phillips, and Charlie Paterson

Pharmaco Inc., a data-driven innovator, took an ambitious approach with its flagship molecule, AlpHa001, targeting a common yet underdiagnosed condition that’s notoriously hard to distinguish. Preparing for Phase 2b, the team leveraged the full power of modern data science—combining unstructured health records, commercial datasets, and AI-driven signal detection.

AI models pinpointed trial-naïve patients across eight countries using creative, multi-source inputs: fitness and nutrition app data, location and credit card trends, online activity patterns, and EMR verification. The result was a patient list seemingly tailored for success.

But when automated outreach began, the response was silence. Spam filters intervened, privacy laws blocked messaging, outdated contact data led to dead ends, and digital fatigue dulled engagement. The AI had identified patterns, not people.

This scenario hasn’t happened — yet. But it reflects a cautionary future where data-driven recruitment, if used without discipline, risks creating noise instead of insight. Oversaturation and mistrust could erode the promise of digital transformation in clinical trials.

In this article, we explore how to prevent that outcome and how to design ethical, effective, and patient-centric AI-powered recruitment pipelines that truly connect the right patients to the right trials.