Protocol-Specific Inclusion/Exclusion Criteria Screening Automation

Manual screening of patient records against multi-parameter trial protocols is a costly, slow, and error-prone bottleneck. It delays enrollment, inflates site labor costs, and introduces inconsistency that compromises study integrity. A custom automation workflow replaces this manual review with an orchestrated system of agents that ingest EHR, lab, and genomic data, apply protocol logic with temporal reasoning, and flag candidates with audit-ready rationale. The operational upside comes from reducing screening time from hours to minutes per patient, improving screening accuracy, and allowing clinical staff to focus on high-value review of complex edge cases rather than repetitive data checking.

Implementation requires integrating with Epic or Cerner EHRs, a CTMS like Veeva, and EDC systems. The core is a rules orchestrator (e.g., LangGraph) managing specialized agents for lab ranges, medication history, and temporal conditions. Controls are critical: every automated flag requires an explainability report citing source data, all ‘soft’ exclusions route to a human review queue in the CTMS, and the rule set is version-controlled with IRB approval gates. Rollout is phased, starting with a single protocol arm, using synthetic data for validation, and incorporating site coordinator feedback to tune exception handling before scaling across the trial portfolio.

This workflow automates the most labor-intensive bottleneck in clinical trial startup: manually comparing patient EHRs against dense, multi-parameter protocol criteria. The operational upside comes from reducing screening time from hours per patient to minutes, accelerating enrollment velocity, and eliminating costly inconsistencies in manual review. It directly impacts trial economics by shortening time-to-first-patient-in and improving site resource utilization, turning a clinical operations constraint into a strategic advantage.

Implementation integrates with Epic or Cerner via FHIR APIs, feeding structured data into a LangGraph-based orchestrator that dispatches to specialized agents (e.g., for lab ranges, prior therapies). Each agent executes against a protocol model, with results aggregated by a scoring engine. Controls include physician-in-the-loop review gates for positive matches, automated explanation reports for auditability, and continuous monitoring for data drift or logic errors within the eligibility rules. The system is deployed as a containerized microservice layer adjacent to the EDC and CTMS.

Phase 1 establishes a non-production pilot, ingesting de-identified EHR data (Epic, Cerner) into a rules engine to validate screening logic against historical trial protocols. This sandbox environment allows for tuning temporal logic (e.g., 'no chemotherapy within 6 months'), testing NLP agents on clinical narratives, and calibrating confidence thresholds without touching live PHI. The goal is a validated, auditable screening algorithm and a clear ROI projection based on manual review time saved per patient record.

Phase 2 connects read-only APIs to live EHRs at a pilot site, routing AI-flagged candidates into a physician review UI within the existing clinical workflow (e.g., Epic Hyperspace). Each flag includes an explainability report citing source data. Phase 3 scales the orchestration layer across multiple sites and integrates with CTMS (Veeva) and EDC systems for automated pre-screening data transfer, turning a point solution into an operational system that reduces screening labor by 70% and cuts enrollment cycle time.

This workflow automates the most labor-intensive bottleneck in clinical trial enrollment: manually comparing patient EHRs against dozens of protocol-specific inclusion/exclusion (I/E) criteria. It replaces error-prone, repetitive chart review with a scalable orchestration layer that ingests structured and unstructured data from Epic, Cerner, and lab systems. The operational upside comes from reducing screening time from hours to minutes per patient, accelerating site activation, and improving match consistency across decentralized research networks, directly impacting trial timelines and cost.

Implementation requires deploying agents for criteria parsing, temporal logic handling (e.g., 'condition not present in last 6 months'), and evidence retrieval. The architecture integrates with Veeva Vault CTMS or Medidata RAVE for audit trails and must include governance controls: automated bias detection in scoring algorithms, human-in-the-loop review gates for borderline cases, and explanation reports for every eligibility flag. Phased rollout starts with retrospective data validation against known cohorts before live deployment at pilot sites.

This workflow automates the high-cost bottleneck of manually comparing patient EHR, genomic, and lab data against dozens of protocol-specific inclusion/exclusion (I/E) criteria. It replaces error-prone, repetitive chart review with a multi-agent system that ingests data from Epic or Cerner, applies temporal logic (e.g., 'condition X within 6 months of treatment Y'), and outputs a scored eligibility matrix. The operational upside is direct: reducing screening labor by 70-80%, accelerating site activation, and improving consistency across reviewers, directly impacting enrollment timelines and trial economics.

Implementation requires orchestrating LangGraph or a custom rules engine with EHR APIs (FHIR/HL7), a vector store for protocol documents, and a human-in-the-loop UI integrated into clinical systems like Veeva CTMS. Critical controls include configurable confidence thresholds for auto-approval, immutable audit logs of all agent decisions with rationale, and scheduled bias checks on screening outcomes. The build must handle data quality exceptions gracefully, routing ambiguous cases to designated reviewers while preserving a complete chain of custody for regulatory inspection.