Rare Disease Patient Finding via Natural Language Phenotyping

Manual chart review for rare disease patients is a costly, slow, and error-prone needle-in-a-haystack search. This custom workflow automates the discovery by deploying natural language phenotyping agents against clinical narratives in Epic, Cerner, and other EHRs. It uses ontology-driven search to interpret complex symptom descriptions, analyze family history, and identify probands, turning a years-long manual process into a systematic, queryable operation. The business value is measured in accelerated trial enrollment timelines and dramatically reduced screening labor costs.

Implementation requires integrating with hospital FHIR APIs or data warehouses, deploying containerized agents for NLP and ontology mapping, and establishing a secure review queue in a system like ServiceNow. Critical controls include bias monitoring in phenotype logic, audit trails for all automated flags, and physician-in-the-loop validation before any patient contact. The architecture must be privacy-by-design, often operating via federated query to avoid moving PHI, ensuring compliance while delivering a scalable recruitment engine for sponsors and CROs.

This engine automates the fundamental bottleneck in rare disease research: finding the proverbial needle in a haystack by manually reviewing millions of clinical notes. The operational upside is measured in years saved per patient identified, directly accelerating trial enrollment and reducing sponsor costs. The architecture ingests narrative data from Epic, Cerner, and other EHRs via FHIR APIs and secure file transfers, applying ontology-driven search and family history analysis to surface potential probands.

Implementation requires a LangGraph or custom orchestrator to manage agent handoffs, state, and exception routing. Each agent is a containerized service with retrieval-augmented generation (RAG) over medical ontologies like HPO and UMLS. Controls include confidence scoring gates, audit logging for all agent decisions, and integration with physician review platforms like Epic Rover or custom dashboards. The final output is a privacy-preserving tokenized patient list, ready for site outreach under governed Data Use Agreements.

Phase 1 establishes a secure, read-only data pipeline from a single Epic or Cerner EHR instance into a private cloud environment. We deploy initial NLP agents for high-confidence phenotype extraction (e.g., specific ICD-10 codes, lab mentions) and a basic review dashboard for genetic counselors. This controlled pilot validates data quality, agent accuracy, and clinician workflow without impacting live operations, delivering a measurable reduction in manual chart review for a defined sub-population within 8-12 weeks.

Phase 2 scales the federated query layer to additional hospital networks, deploying more sophisticated agents for family history analysis and proband identification from clinical narratives. Integration with an IRB-managed consent orchestration system begins. Phase 3 focuses on operational integration, connecting the stratified patient lists to downstream systems like CTMS for site activation and eConsent platforms, while implementing continuous monitoring for algorithmic bias and data quality drift. Each phase includes defined success metrics, governance checkpoints, and rollback procedures.

Operationalizing rare disease phenotyping requires a controlled architecture where automated agents never act autonomously on patient data. The core workflow ingests clinical narratives from Epic or Cerner via secure APIs, applies ontology-driven NLP agents for phenotype extraction, and outputs ranked candidate lists. All data movement is logged against Data Use Agreements (DUAs), and patient identifiers are tokenized until an authorized physician review gate. The business value is turning a multi-year, manual search into a systematic process that scales across hospital networks while preserving HIPAA and GDPR compliance by design.

Implementation follows a phased rollout, starting with a single disease ontology and one health system to validate precision and recall against known patient registries. Observability is built into each agent, logging decision rationale and confidence scores for audit. The second phase expands disease coverage and integrates federated query logic for cross-institutional search without moving PHI. The final production phase adds continuous bias monitoring and synthetic data generation for algorithm stress-testing. This controlled approach de-risks deployment, ensures physician trust, and creates a defensible pipeline for IRB and regulatory review.

This workflow automates the high-stakes search for rare disease patients by deploying NLP agents across clinical narratives, but its real operational value is in eliminating the manual, error-prone compliance overhead that stalls research. By architecting compliance checks and audit logging directly into the data ingestion, processing, and output layers, sponsors and CROs can accelerate patient finding while generating the immutable evidence trail required for IRB review, HIPAA/GDPR audits, and FDA inspections. The savings come from reducing legal and regulatory risk, cutting manual audit preparation from weeks to hours, and enabling scalable, repeatable operations across decentralized hospital networks without compliance bottlenecks.

Implementation requires orchestrating specialized agents for ontology-driven search and family history analysis within a framework like LangGraph, integrated with hospital EHRs (Epic, Cerner) via privacy-preserving APIs. Each agent's decision—from data access to patient flagging—must be logged with a cryptographically sealed rationale. The architecture must include automated checks against Data Use Agreements (DUAs) and dynamic consent stipulations before any record is processed. Rollout sequencing should prioritize a pilot network with synthetic data validation, ensuring the audit trail meets the evidentiary standards of regulators before scaling to live PHI across multiple institutions.