AI System for Clinical Trials (Patient Selection, Monitoring)

Developing AI System for Clinical Trials — Patient Selection and Monitoring

Clinical trials — most expensive drug development stage. Phase III: $200M–$1B, 3–7 years. 30% trials fail due to patient recruitment issues, 50% sites enroll less than 70% planned participants. AI solves this systematically.

Patient Recruitment

Automatic EHR Screening by Inclusion/Exclusion Criteria

Each clinical trial has dozens inclusion/exclusion criteria written in medical English. Task: find suitable patients in hospital EMR database.

NLP system:

1. Criteria parsing: structured representation via clinical concept extraction (SNOMED, LOINC, RxNorm)
2. EHR matching: searching patients meeting all criteria
3. Ranking: prioritizing by likelihood of passing pre-screening

In practice: manual screening of 1000 patients = 100–200 hours. NLP screening = 2–5 minutes with recall >92% on criteria.

Generalizability Analysis

AI analyzes how representative trial population to real patient population. Identifying systematic exclusion (e.g., historically all heart failure trials excluded women and elderly).

Completion Rate Prediction

Model predicts probability specific patient completes trial (vs. dropout):

• Geographic accessibility
• Socioeconomic factors
• History of compliance with previous prescriptions
• Number of visits and frequency

Selecting high-compliance patients first reduces dropout rate and speeds enrollment.

Monitoring During Trial

Safety Signal Detection

Real-time adverse events (AE) analysis as data arrives. Traditionally: monitors manually review CRF (Case Report Forms). AI:

• Automatic AE coding by MedDRA
• Detecting potential serious AEs in source documents (NLP)
• Statistical signal detection: disproportionality analysis (PRR, ROR) for early safety signal detection

Protocol Deviation Detection

NLP + rule checking: automatic protocol deviation identification from EMR and ePRO data. Example: patient took prohibited medication per pharmacy records.

ePRO Data Quality

Electronic Patient-Reported Outcomes: predicting missing/implausible responses. Models: temporal patterns, out-of-range answers, anomalous response speed (too fast → didn't read).

Adaptive Trial Design Support

For adaptive trials: Bayesian statistics + simulations for interim decision justification (continue, modify, stop). AI component: fast operating characteristics simulations under different scenarios.

Site Network Optimization

Site Performance Prediction

Predicting enrollment rate and data quality per site:

• Historical performance on previous trials
• Size and quality of patient population
• Staff experience
• Infrastructure (EMR, regulator communication)

Site selection became data-driven instead of based on CRO connections.

Country Feasibility

AI analyzes regulatory timelines, patient pool size, cost, approval speed by country → optimal country mix for multinational trial.

Synthetic Control Arms

Instead of traditional control group (placebo) — building synthetic control from real patients (RWD - Real World Data) not receiving study drug. Propensity score matching + ML for maximum comparability.

Regulatory status: FDA and EMA accept synthetic controls for orphan diseases and accelerated pathways under certain conditions. Savings: excluding 30–50% control group participants = cost reduction and ethical placebo issues mitigation.

Development timeline for patient recruitment AI system: 3–5 months for specific therapeutic area with integration into 3–5 EMR systems.