Clinical Trial Matching

Clinical trial matching is the use of AI to automatically connect patients with appropriate clinical trials — analyzing patient demographics, medical history, diagnoses, biomarkers, and trial eligibility criteria to identify suitable trial opportunities, accelerating enrollment and ensuring more patients access experimental treatments.

What Is Clinical Trial Matching?

• Definition: AI-powered matching of patients to eligible clinical trials.
• Input: Patient data (EHR, labs, genomics) + trial eligibility criteria.
• Output: Ranked list of matching trials with eligibility assessment.
• Goal: Faster enrollment, broader access, more representative trials.

Why Clinical Trial Matching Matters

• Enrollment Crisis: 80% of trials delayed due to enrollment issues.
• Awareness Gap: 85% of patients unaware of relevant trials.
• Complexity: Average trial has 30+ eligibility criteria per protocol.
• Manual Burden: Manual screening takes 2+ hours per patient per trial.
• Diversity: Underrepresentation of minorities in clinical trials.
• Cost: Failed enrollment costs pharma industry $37B annually.

How AI Matching Works

Patient Profile Extraction:
- Source: EHR, lab results, pathology reports, genomic data.
- NLP: Extract diagnoses, medications, labs, procedures from unstructured notes.
- Structured Data: Demographics, vitals, biomarkers from EHR fields.
- Temporal: Consider timing of diagnoses, treatments, disease progression.

Trial Criteria Parsing:
- Source: ClinicalTrials.gov, trial protocols, sponsor databases.
- NLP: Parse free-text eligibility criteria into structured rules.
- Criteria Types: Inclusion (must have) and exclusion (must not have).
- Challenge: Criteria often ambiguous, complex, and nested.

Matching Algorithm:
- Rule-Based: Check each criterion against patient data.
- ML-Based: Learn from past enrollment decisions.
- Hybrid: Rules for clear criteria + ML for ambiguous ones.
- Scoring: Rank trials by match quality and relevance.

Key Challenges

• Data Completeness: Patient records may lack required information.
• Criteria Ambiguity: "Recent surgery" — how recent? Which surgery?
• Temporal Reasoning: Must consider timing, sequences, disease stages.
• Lab Interpretation: Normal ranges, units, timing of measurements.
• Geographic Constraints: Trial site location vs. patient location.

Impact & Benefits

• Speed: Reduce screening time from hours to minutes per patient.
• Volume: Screen entire hospital population against all active trials.
• Diversity: Identify eligible patients from underrepresented groups.
• Revenue: Clinical trials generate $7K-10K per enrolled patient for sites.

Tools & Platforms

• Commercial: Tempus, Deep 6 AI, TrialScope, Mendel.ai, Criteria.
• Academic: CHIA (parsing eligibility criteria), Cohort Discovery.
• Data Sources: ClinicalTrials.gov, AACT database, sponsor databases.
• EHR Integration: Epic, Cerner with trial matching modules.

Clinical trial matching is critical for medical research — AI eliminates the bottleneck of patient enrollment by automatically identifying eligible candidates, ensuring more patients access innovative treatments and clinical trials achieve representative, timely enrollment.