Longitudinal Patient History Eligibility Check Automation

Manual review of a patient's longitudinal history to verify trial eligibility is a major bottleneck, consuming hundreds of clinician hours per study. Staff must mentally reconstruct timelines from fragmented EHR notes, lab trends, and treatment records, a process prone to oversight of prior conditions or protocol-violating therapies. This operational drag directly extends enrollment timelines, increases screening costs, and introduces eligibility errors that can jeopardize study integrity and regulatory approval. Automating this temporal reasoning is a direct lever on trial velocity and quality.

Implementation requires orchestrating agents for temporal data fusion from Epic or Cerner, applying protocol-specific logic to sequences of events, and generating an auditable eligibility rationale. The workflow integrates with Clinical Trial Management Systems (CTMS) to update candidate status and routes low-confidence cases or positive matches to a human-in-the-loop review dashboard within the site's workflow. Controls include bias monitoring on the reasoning models, continuous data quality checks, and immutable audit logs for every automated decision to satisfy IRB and regulatory scrutiny.

This workflow automates the complex, manual review of a patient's longitudinal medical history to determine trial eligibility. It ingests and fuses data from disparate EHR systems like Epic and Cerner, along with lab trends and treatment histories, applying temporal logic to check for prior conditions, washout periods, and contraindications. The automation eliminates weeks of manual chart abstraction per patient, directly reducing enrollment delays and improving screening accuracy for sponsors and CROs.

Implementation requires orchestrating agents for data harmonization, a rules engine for protocol criteria, and an LLM layer for generating audit-ready rationales. The architecture integrates with CTMS and EDC systems like Veeva, featuring governance controls for bias detection, exception routing, and continuous data quality monitoring. This creates a scalable, defensible system that accelerates cohort identification while preserving clinical oversight and regulatory compliance.

Phase 1 establishes a foundational data pipeline, focusing on high-fidelity ingestion and normalization of structured EHR data (Epic, Cerner) and discrete lab results. This initial orchestration layer, built with LangGraph or a custom Python service, creates a single longitudinal patient timeline. The immediate ROI comes from eliminating manual data aggregation, reducing chart review time by 60-80% for initial screening, and providing a clean, auditable data foundation for subsequent AI agents.

Phase 2 introduces intelligence, layering on NLP agents to extract phenotypes from clinical notes and a temporal logic engine to evaluate complex, time-based inclusion/exclusion rules (e.g., 'condition X resolved more than 6 months prior to treatment Y'). Phase 3 focuses on production hardening, integrating a physician review interface with explainable AI flags and embedding governance controls for audit trails and bias monitoring. This staged approach controls cost, validates each component's clinical utility, and ensures the final workflow meets both operational and compliance requirements.

This workflow automates the error-prone manual review of a patient's longitudinal history across EHRs like Epic and Cerner, checking for prior conditions, treatments, and lab trends against complex trial protocols. The operational upside is a 70-80% reduction in screening time per patient and higher-quality candidate identification, directly reducing costly enrollment delays. Implementation requires a multi-agent architecture with temporal reasoning logic, integrated with clinical data warehouses and governed by strict audit controls to ensure clinical safety and regulatory defensibility.

Rollout must be phased, starting with a single protocol and site to validate data pipelines, agent accuracy, and physician feedback loops before scaling. Critical controls include real-time DUA compliance checks, automated bias detection in screening logic, and the generation of audit-ready explanation reports for every eligibility flag. The final architecture integrates with CTMS and EDC systems, creating a closed-loop workflow that improves enrollment velocity while maintaining the necessary human-in-the-loop oversight for high-stakes clinical decisions.

This workflow automates the high-cost, high-error process of manually reviewing a patient's longitudinal history for trial eligibility. It ingests structured and unstructured data from Epic, Cerner, and other EHRs, applying temporal logic to check for prior conditions, treatment sequences, and lab trends against protocol criteria. The operational upside comes from reducing screening time from hours to minutes per patient, improving enrollment velocity, and eliminating oversights from manual fatigue. Implementation requires orchestrating NLP agents for note parsing, a temporal reasoning engine, and integration APIs to clinical data warehouses.

Implementation centers on a LangGraph or custom orchestrator that sequences data fusion, invokes specialized agents, and enforces HIPAA and 21 CFR Part 11 controls. Data must be tokenized or de-identified at ingress, with all access logged. The temporal agent constructs a patient timeline, evaluating criteria like "condition X must not have occurred within 6 months of treatment Y." Outputs route to a human-in-the-loop review dashboard with explanation reports. Rollout requires phased validation against historical screens, integrating with IRB systems for consent checks, and continuous monitoring for algorithmic drift or bias in eligibility determinations.