Deep 6 AI

Applies natural language processing to health system records — notes, pathology, imaging reports — to surface patients who meet criteria that structured coding misses, then supports site-side outreach.

Enterprise software agreements with health systems and sponsors.

Whatever the structure, convert it to cost per randomized patient before you compare proposals. Cost per referral is the number vendors prefer to quote and the number that tells you least.

• Finds patients invisible to ICD-code queries
• Publicly focused on oncology and other criteria-heavy indications
• Supports feasibility with counts from real records rather than estimates

• Value depends on your sites being connected health systems
• Identifying a patient is not consenting one; site outreach still has to happen

Worth it when: Oncology and precision-medicine trials with complex eligibility criteria.

Not worth it when: Studies at small independent sites without integrated record systems.

• Elligo Health ResearchBrings trials to community physician practices and their patient records.
• TrialbeePatient matching and orchestration across multiple referral sources.
• CitelineTrial intelligence and site/investigator data — not a recruitment vendor.

• Deep 6 AI vs Citeline — Patient-level record mining versus market-level trial data.
• Trialbee vs Deep 6 AI — Referral orchestration versus patient identification.

Ask for the referral-to-randomization ratio on three studies resembling yours, in the same indication and the same countries. If a vendor will not give it, that is the answer. If your enrollment forecast itself is the shaky part, our sister publication Rock Enroll covers forecasting and rescue in depth.