AI-ENABLED CLINICAL TRIAL MATCHING
Toward an AI Reasoning-Enabled System for Patient-Clinical Trial Matching
This paper presents a secure, scalable AI-augmented system for patient-clinical trial matching, leveraging reasoning-enabled large language models (LLMs) like DeepSeek-R1. It addresses key challenges such as integrating heterogeneous EHR data, providing interpretable reasoning chains, and maintaining rigorous security standards. The system moves beyond binary classification to generate structured eligibility assessments, identifies matches, and offers actionable recommendations, transforming patient eligibility into a dynamic state. The goal is to reduce coordinator burden, intelligently broaden the set of trials considered, and guarantee comprehensive auditability, accelerating medical discovery.
Executive Impact
Key Impact Metrics
Our AI-powered system delivers quantifiable improvements across critical aspects of clinical trial operations:
92%
USMLE ACCURACY
100,000
WORDS CONTEXT WINDOW
950
AVG. TRIALS PER PATIENT
10x
TRIAL VOLUME INCREASE
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Patient Information Extraction
ClinicalTrials.gov API Call
Patient-Trial Eligibility Assessment
Detailed Reasoning Output
Patient Information Extraction (PIE)
The system identifies and extracts 14 key clinical data categories from EHR data, including diagnoses, patient demographics, treatment history, and biomarkers. It uses a templated prompt to guide the model's extraction logic, ensuring structured input for trial identification and eligibility assessment.
ClinicalTrials.gov API Call
A multi-tiered keyword search strategy balances specificity and comprehensiveness. It uses primary diagnoses, then broader synonyms to retrieve candidate trials, prioritizing recall over precision. Minimum criteria (recruiting status, demographics, location) narrow results. The system aggregates results from multiple queries into structured JSON objects.
Patient-Trial Eligibility Assessment (PTEE)
Using the Patient-Trial Eligibility Evaluator (PTEE) template, the system assesses patient-trial compatibility based on minimum eligibility, inclusion, and exclusion criteria. It provides reasoning, certainty levels, and identifies data gaps or future eligibility recommendations (e.g., 'Eligible Now', 'Could Be Eligible in Future', 'Not Eligible', 'Need More Information').
Detailed Reasoning Output
Outputs are JSON files, formatted into Word/PDF reports, preserving reasoning chains for human review. Each report includes metadata for auditing. This transparency supports evidence-based decision-making and fosters clinician trust, shifting expert review to focus on ambiguous cases and high-certainty matches.
Enterprise Process Flow
Ingested EHR
→
Step 1: Patient Information Extraction
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Step 2: Retrieve Candidate Trials
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Step 3: Eligibility Assessment
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Step 4: Detailed Reasoning Output
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Outputs for Human-in-the-Loop Review
10x
Increase in candidate trials identified per patient through synonym-based expansion.
| Feature | Our AI System | Traditional Manual Screening |
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| Data Integration |
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| Trial Search Scope |
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| Eligibility Assessment |
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| Transparency & Auditability |
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| Efficiency & Scalability |
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Dynamic Eligibility in Practice: Pancreatic Adenocarcinoma Trial
A synthetic patient with pancreatic adenocarcinoma was evaluated against trial NCT05764720. The patient had completed only two weeks of FOLFIRINOX, but the trial required at least two months. Instead of immediate exclusion, the system assigned a 'Could Be Eligible in Future' status with medium certainty.
Outcome: The model's reasoning distinguished this time-dependent prerequisite from other unresolved factors (e.g., breath-hold capability, therapy interruption feasibility, confirmatory imaging). This temporal reasoning capability prevents premature exclusion and provides actionable recommendations to guide clinical decision-making, offering insights into determination and suggesting next steps for advancing patients toward eligibility. The system correctly identifies that eligibility is a dynamic state.
Highlight: 'Complete 2 months of FOLFIRINOX chemotherapy (currently at cycle 1/12)'.
Advanced ROI Calculator
Our AI-powered trial matching system significantly reduces the manual effort and time spent by clinical coordinators. By automating the initial screening and providing detailed, actionable insights, it frees up valuable human resources, allowing staff to focus on critical patient care and complex case management. This leads to substantial operational cost savings and increased trial enrollment efficiency.
Implementation Roadmap
Our phased approach ensures a smooth transition and continuous improvement, maximizing your ROI and minimizing disruption.
Integration of Source Citation
Enhance system to explicitly reference precise source locations of extracted data (report date, page number) for improved traceability and auditability.
Intuitive Web-Based Interface Development
Create a dedicated interface for expert validation and system improvements, facilitating efficient human review and feedback capture.
Reinforcement Learning for Performance Improvement
Systematically capture expert review data to refine model performance over time, establishing an iterative improvement loop.
Public Release on GitHub
Generalized pipeline will be released on an open platform to promote accessibility and community-driven improvements.
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Our AI-enabled patient-trial matching system is designed to streamline your workflows, accelerate research, and connect more patients with life-changing opportunities. Schedule a personalized consultation to see how we can tailor this solution to your institution's specific needs.
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