Patient Recruitment

Use AI to automate patient eligibility screening and optimize clinical trial recruitment.

Business impact

• Enrollment rate — Increases by identifying eligible patients more quickly and accurately
• Time to enrollment — Decreases due to automated screening and faster patient matching
• Cost per enrolled patient — Lowers by reducing manual pre-screening and minimizing ineligible patient outreach
• Trial execution efficiency — Improves through optimized recruitment workflows and reduced delays

Data requirements

• Electronic Health Records (EHRs) (Structured) — Used to identify patient medical history and eligibility criteria
• Clinical trial protocols and eligibility criteria (Text) — Parsed via NLP to interpret inclusion/exclusion rules
• Claims and insurance data (Structured) — Provides additional patient health and demographic information
• Genomic and biomarker data (Numeric) — Enables precision matching based on molecular profiles
• Patient-reported outcomes and social determinants (Text) — Supports personalized recruitment and retention strategies

AI methods and techniques

• Predictive AI — Predicts patient eligibility, adherence, and retention likelihood
• Generative AI — Interprets free-text eligibility criteria and generates screening decisions
• Symbolic AI — Applies rule-based logic to enforce clinical trial inclusion/exclusion criteria

AI models and model families

GPT-4o, Claude, Llama

Industries