Lesson 3 of 3•AI for Clinical Trial Design0 of 3 complete (0%)
10 min read
AI for Patient Recruitment & Retention
What you'll learn
- 1Apply AI to identify and match patients to clinical trials using EHR and claims data
- 2Use AI-driven predictive models to forecast enrollment rates and identify bottlenecks early
- 3Build AI-powered retention strategies that predict and prevent patient dropout
- 4Understand the ethical and privacy considerations of AI-based patient matching
# AI for Patient Recruitment & Retention
Patient enrollment is the single largest bottleneck in clinical development. The average Phase 3 trial takes 12-18 months to fully enroll, and 80% of trials fail to meet their initial enrollment timelines. This adds an estimated $600K-$8M in costs per day of delay for a blockbuster drug.
The Patient Matching Challenge
Clinical trial eligibility criteria define a narrow patient population. Matching real patients to these criteria requires screening medical records, lab results, imaging reports, concomitant medications, and medical histories — a process that is overwhelmingly manual at most sites.
AI can transform this by analyzing Electronic Health Record (EHR) data at scale:
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What you'll learn:
- Apply AI to identify and match patients to clinical trials using EHR and claims data
- Use AI-driven predictive models to forecast enrollment rates and identify bottlenecks early
- Build AI-powered retention strategies that predict and prevent patient dropout