Phenotype-Driven Patient Cohort Discovery Automation

Manual chart review to find patients matching a novel phenotypic description is a needle-in-a-haystack problem, costing research teams months of effort and delaying trial starts. This custom workflow automates that search by deploying NLP agents to parse unstructured clinical narratives in EHRs like Epic or Cerner, linking extracted entities to biomedical ontologies (e.g., HPO, SNOMED CT). The architecture orchestrates semantic search across decentralized data, replacing repetitive manual screening with a scalable, queryable system that directly reduces enrollment timelines and improves candidate precision for sponsors and CROs.

Implementation integrates with clinical data warehouses and uses a framework like LangGraph for agent orchestration, ensuring each patient match is backed by retrievable evidence. The workflow includes mandatory physician review gates and an immutable audit trail for regulatory defensibility. Key controls govern data access per DUAs and patient consent, while continuous monitoring checks for algorithmic bias and data quality drift. The result is an operational architecture that turns phenotypic discovery from a research bottleneck into a repeatable, governed process.

This workflow automates the bottleneck of manually translating published phenotypic descriptions into executable queries across fragmented EHR and clinical narrative data. The operational upside comes from reducing cohort identification from weeks to hours, directly accelerating study startup and enrollment. Savings are realized through the elimination of hundreds of hours of manual chart review by clinical research coordinators and data managers, while improving match precision over keyword-based searches.

Implementation integrates with Epic or Cerner via HL7/FHIR APIs and uses LangGraph for agent orchestration. The architecture includes mandatory human-in-the-loop approval gates after the ranking engine, with explainability reports generated for each candidate. Controls are required for audit trails, bias monitoring in scoring algorithms, and continuous data quality checks against source systems to ensure clinical plausibility before physician review.

Phase 1 establishes the core data pipeline, ingesting and harmonizing EHR data from Epic or Cerner with clinical narratives. We deploy NLP agents for entity extraction and link them to ontologies like SNOMED-CT, creating a queryable patient index. This initial build delivers immediate value by automating manual chart review for a single protocol, proving the architecture's accuracy and setting up governance controls for data quality and physician review gates before scaling.

Phase 2 layers on the orchestration logic, deploying LangGraph or a custom agent framework to execute complex phenotype queries across the indexed data. We integrate the output with EDC systems like Medidata Rave and CTMS like Veeva Vault, automating candidate list generation. The final phase scales the workflow across multiple protocols and therapeutic areas, while implementing continuous monitoring for algorithmic bias and DUA compliance, transforming the build into a production-ready, enterprise research asset.

This workflow automates the identification of patients matching complex phenotypic descriptions from literature or novel study designs, directly addressing the bottleneck of manual chart review for rare disease or precision medicine trials. The operational upside comes from accelerating cohort building from months to days, while improving match quality through semantic search across structured EHR data and unstructured clinical narratives. Implementation requires integrating with Epic or Cerner via HL7/FHIR, deploying NLP agents for entity extraction, and linking to ontologies like HPO or SNOMED CT for logic-based phenotype matching.

Rollout must be phased, beginning with a single-site pilot using synthetic or historical data to validate logic and bias controls before live PHI processing. A governance layer mandates every automated eligibility flag be accompanied by an AI-generated explanation report for physician review, with all decisions logged to an immutable audit trail for regulatory inspection. Exception handling routes ambiguous cases to human reviewers via integrated systems like Epic's In Basket, while continuous compliance monitors scan for DUA and consent violations, ensuring the workflow reduces enrollment delays without compromising ethical or legal standards.