Clinical Trial Recruitment & Patient Eligibility Screening
AI clinical trial eligibility agent uses Amazon Bedrock and RAG to organize authorized patient screening and prepare evidence-linked summaries for research teams.
Business Process Challenges
A clinical research organization in Italy wanted to make participant identification and preliminary eligibility review more efficient. Research teams needed to examine authorized patient records alongside trial protocols and detailed inclusion and exclusion criteria.
Manually comparing patient information against trial requirements involved reviewing multiple clinical documents and healthcare systems. This created administrative effort and slowed the preparation of candidate lists for researcher assessment.
The organization also needed to identify missing, outdated or conflicting patient information while protecting sensitive clinical data. Eligibility screening and enrollment decisions had to remain under qualified human oversight.
Our Strategic Approach
Softree proposed an AI-powered Clinical Trial Recruitment & Eligibility Agent using Amazon Bedrock, Amazon Bedrock AgentCore and retrieval-augmented generation (RAG).
The solution connects approved trial documentation with authorized patient information to support preliminary screening through six stages:
- Trial Criteria Retrieval: Retrieves approved study protocols, inclusion and exclusion criteria, and screening guidance from Amazon Bedrock Knowledge Bases.
- Patient Record Intake: Accesses authorized clinical records through approved integrations and uses Amazon Textract to extract information from supported documents.
- Preliminary Eligibility Analysis: Compares available patient information against individual criteria and classifies findings as potential match, potential exclusion or insufficient information.
- Missing Information Detection: Identifies absent, outdated or conflicting details requiring confirmation.
- Candidate Summary Generation: Produces structured, evidence-linked summaries containing relevant criteria, supporting record references and uncertainties.
- Research-Team Review: Routes candidate summaries to authorized clinical research staff for verification and final eligibility decisions.
The architecture combines Amazon Bedrock, AgentCore, Amazon S3, DynamoDB, AWS Lambda, Step Functions, Python, FastAPI, React, TypeScript, AWS IAM, AWS KMS, CloudWatch and supported FHIR APIs.
Our Engineering & Delivery Process
Our structured project delivery approach takes you from discovery and planning to development, testing, and deployment, ensuring quality, transparency, and predictable outcomes at every stage.
Discovery & Architecture
Deep-dive technical audit of existing systems, security baselines, API mapping, and comprehensive cloud target blueprinting.
UX & System Prototyping
Role-based user journeys, wireframing, high-fidelity clickable Figma prototypes, and API contract specifications prior to build.
Agile Sprint Engineering
Modular development sprints delivering clean, shippable code with automated CI/CD pipelines and continuous testing.
QA & Security Hardening
End-to-end regression suites, cross-device verification, load stress audits, and penetration testing for enterprise resilience.
Production Launch & Scale
Zero-downtime blue-green production deployment, real-time telemetry, administrator training runbooks, and ongoing SLA support.
Explore the Solution Through visuals
What changed for the client.
The proposed solution is designed to make preliminary clinical trial screening more structured, consistent and traceable.
Expected pilot benefits include:
- Reduced manual effort in locating and comparing trial criteria with patient information.
- More consistent preliminary screening summaries for research teams.
- Earlier identification of missing or conflicting clinical information.
- Clearer traceability from screening findings to supporting patient records.
- More time for qualified research staff to focus on clinical assessment and participant engagement.
These outcomes are intended pilot benefits, not measured client results. Baselines and performance targets should be established with the research organization before implementation.
The agent supports research teams without diagnosing patients or making enrollment decisions. Qualified clinical personnel retain responsibility for screening and participant eligibility, with deployment designed around GDPR, healthcare confidentiality and institutional governance requirements.
The numbers behind the rollout.
The full integration layer.
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