Automation Workflow for Identifying Potential Trial Participants from Real-World Data

Manual screening of EHRs against trial protocols is a massive operational bottleneck, delaying enrollment and inflating study costs. A custom automation workflow solves this by deploying NLP agents to continuously interpret inclusion/exclusion criteria against fused patient data—longitudinal records, labs, imaging notes, and medications. This creates a privacy-preserving, always-on recruitment engine that surfaces eligible patients to research coordinators within hours, not weeks, directly compressing trial timelines and improving site economics through higher screening throughput and reduced coordinator labor.

Implementation focuses on building a secure orchestration layer, typically using LangGraph or a custom microservice, that ingests real-time HL7/FHIR feeds from Epic or Cerner. NLP agents, fine-tuned on clinical language, parse complex trial protocols into executable logic. Matches are scored and routed with confidence thresholds to a human-in-the-loop review queue integrated with clinical trial management systems like Medidata or Veeva. Critical controls include patient privacy filters, audit trails for protocol adherence, and configurable opt-in workflows to ensure compliance and maintain trust.

This workflow directly addresses the multi-million dollar bottleneck of slow clinical trial enrollment. It automates the manual, repetitive process of chart review by research coordinators, replacing it with a continuous, privacy-preserving search across fused EHR, imaging, and lab data. The operational upside comes from compressing enrollment timelines from months to weeks, improving site utilization, and enabling participation in more trials. Savings are realized through reduced coordinator FTE spent on screening and higher per-patient trial revenue from faster activation.

Implementation is built on a LangGraph or similar orchestration framework, with specialized agents for FHIR ingestion, NLP-based criteria interpretation, and de-identification. The core architectural decision is a privacy-by-design layer that performs all candidate matching on tokenized, de-identified data before any re-identification for outreach. Controls include mandatory human review of all automated matches, integrated audit trails for GDPR/HIPAA compliance, and configurable opt-in workflows managed through patient portals. Rollout requires phased integration with the primary EHR (e.g., Epic or Cerner) and the clinical trial management system (CTMS), beginning with a single therapeutic area to validate matching accuracy and coordinator workflow.

This workflow automates the costly, manual bottleneck of screening patient records against trial inclusion/exclusion criteria. By continuously fusing and analyzing EHR data, imaging reports, and clinical notes via NLP agents, it identifies eligible candidates as they become available, directly reducing enrollment timelines from months to days. The operational upside comes from dramatically higher screening throughput, improved site economics, and faster trial completion. Implementation begins with a tightly scoped pilot on a single trial protocol and data source, such as Epic and a specific oncology study, to validate the matching logic and exception handling before scaling.

The architecture centers on a stateful orchestration layer, built with frameworks like LangGraph, that ingests HL7/FHIR feeds and applies a multi-agent system for criteria interpretation. One agent parses complex free-text criteria, another queries structured EHR data, and a third handles temporal logic. All outputs route through a human-in-the-loop approval gate where research coordinators review candidate flags via a dedicated dashboard before any patient contact. Critical controls include robust de-identification before alerting, configurable opt-in workflows, and full audit trails integrated with clinical trial management systems (CTMS) like Medidata or Veeva for seamless operational handoff.

This workflow automates the continuous screening of a health system's patient population against complex trial criteria, accelerating enrollment by identifying eligible candidates in near real-time. The operational upside comes from reducing manual chart review from hours to seconds per patient, directly shortening trial timelines and improving site economics. Implementation requires a privacy-preserving search layer over fused EHR, imaging, and lab data, using NLP agents to interpret inclusion/exclusion logic and integrate with CTMS like Medidata or Veeva.

Governance is enforced through configurable confidence thresholds, mandatory human review for all potential matches, and immutable audit trails logging every agent decision. A phased rollout begins with a single disease cohort and one trial protocol, running in 'shadow mode' to validate accuracy against manual screening before enabling live alerts to research coordinators. Controls include daily reconciliation reports, bias monitoring on exclusion patterns, and strict access logging for PHI accessed during the screening process to ensure HIPAA and ICH-GCP compliance.

This workflow automates the continuous screening of a health system's patient population against complex trial inclusion/exclusion criteria, directly accelerating enrollment timelines and reducing manual chart review by research coordinators. The operational bottleneck is the repetitive, error-prone task of manually cross-referencing fragmented EHR, imaging, and lab data with lengthy protocol documents. Savings come from dramatically shortened recruitment cycles, higher site throughput, and more precise patient matching, which lowers trial costs and speeds time-to-market for new therapies. Implementation requires orchestrating NLP agents to interpret criteria, privacy-preserving search across fused data, and integration with CTMS for candidate management.

Architecturally, the solution is built on an event-driven orchestrator (e.g., LangGraph) that ingests HL7/FHIR feeds from Epic or Cerner, fuses data into a patient 360 view, and routes it through specialized agents for criteria evaluation, privacy checks, and outreach. Critical controls include human-in-the-loop review gates for ambiguous matches, automated data enrichment requests to resolve missing lab values or unstructured notes, and a full audit trail for IRB compliance. Implementation must sequence rollout by therapeutic area, validate NLP interpretation against manual screening for initial protocols, and design monitoring dashboards for match rates, exception volumes, and time-to-alert metrics to ensure operational reliability.