Clinical trial teams face costly delays caused by manual and fragmented eligibility screening. AWS introduces an AI-powered agent framework combining Bedrock AgentCore and HealthLake to automate and accelerate patient eligibility and safety evaluations with human-in-the-loop governance.
- Uses AWS HealthLake to unify clinical data from multiple sources in FHIR format
- AgentCore orchestrates multi-step eligibility and safety evaluations with AI and human review
- Improves reliability and speed of enrollment decisions while supporting compliance
Infrastructure signal
The new clinical trial eligibility agent architecture is built on AWS infrastructure components that optimize data integration, processing, and compliance. AWS HealthLake normalizes clinical records from Electronic Health Records, lab systems, imaging reports, and medication histories into a FHIR-native, queryable data store. This unified clinical data format reduces overhead caused by fragmented and heterogeneous data sources, enabling faster, more reliable data access for AI agents.
Amazon Bedrock AgentCore manages complex multi-agent workflows, handling stages such as patient data assembly, protocol matching, safety signal detection, and recommendation generation. The system uses a knowledge base for protocol criteria and safety guidelines, stored centrally to maintain consistency across evaluations. All agent outputs and clinician interactions are stored for immutable audit trails, supporting regulatory requirements around trial enrollment decisions.
Developer impact
Developers gain a clear framework to implement AI-powered clinical screening with modular agents focused on discrete logic phases. The architecture allows leveraging large language models for reasoning while enabling human-in-the-loop review checkpoints, balancing automation with clinical judgment. This approach facilitates continuous learning and evaluation of AI performance via Amazon Bedrock AgentCore Evaluations, improving accuracy over time.
By standardizing input data on FHIR through HealthLake, developers can build scalable, interoperable APIs that interact with multiple disparate clinical systems without bespoke integrations. This improves developer workflow by reducing data normalization tasks and enabling focus on agent logic and UI for clinician review. Overall, the environment encourages iterative enhancements and rapid deployment cycles in regulated clinical operations.
What teams should watch
Teams responsible for cloud cost management should consider that while unified data ingestion and AI orchestration layers optimize operational efficiency, running multi-step AI agents and maintaining knowledge bases involve steady compute and storage costs. Monitoring agent evaluation outcomes and human interaction volumes will help anticipate scaling needs and control associated drilling costs.
Reliability teams must ensure robust integration between HealthLake FHIR services and AgentCore workflows, as any failure in data normalization or reasoning phases disrupts screening timeliness with costly trial delays. Observability tooling should track end-to-end pipeline health, agent decision confidence scores, and audit trail completeness to rapidly diagnose issues.
Developer and clinical operations teams should monitor evolving trial protocols as these impact knowledge base updates within the agent pipeline. Coordination mechanisms for updating inclusion/exclusion criteria and safety rules centrally will prevent discrepancies in multi-site deployments and help maintain regulatory compliance through consistent AI-assisted decisions.