AI Automation Workflow for Patient Cohort Stratification

Manual chart review for a single multi-site oncology trial can consume over 2,000 person-hours and cost sponsors $150,000+ in CRO fees. A custom orchestration workflow automates the ingestion and evaluation of EHR, genomic, and narrative data against protocol criteria, replacing repetitive human screening with multi-agent logic. The savings come from eliminating the linear, error-prone work of coordinators and data managers, allowing them to focus on high-value exception review and patient engagement.

Traditional patient finding relies on manual site outreach and slow, batch-based queries, often taking 3-6 months to identify a viable cohort. A custom workflow with federated query orchestration and real-time data harmonization enables continuous, parallel screening across decentralized networks. The operational upside is faster site activation, earlier first-patient-in, and the ability to rescue enrollment timelines for at-risk studies, directly impacting development speed and patent clock.

Manual screening is inconsistent and misses nuanced temporal logic or biomarker combinations, leading to high screen-failure rates (often 30-50%). An agentic workflow applies consistent, complex logic—checking longitudinal history, genomic variant pathogenicity, and phenotype ontologies—to surface higher-fidelity matches. This reduces costly screen failures and protocol deviations, improves statistical power, and delivers cleaner data, translating to lower per-patient trial costs and stronger regulatory submissions.

Expanding patient finding beyond a single hospital system is operationally prohibitive due to data silos and compliance overhead. A custom architecture combines Privacy-Preserving Record Linkage (PPRL), granular consent orchestration, and automated DUA compliance checks. This creates a scalable, federated network model without moving PHI, turning patient finding from a constrained, site-by-site effort into a systematic enterprise capability that supports global trials and rare disease searches.

The production workflow is built on a LangGraph or Temporal orchestration layer coordinating specialized agents: Data Harmonization Agents (mapping Epic/Cerner feeds), NLP Phenotyping Agents (extracting from clinical notes), Genomic Annotation Agents, and Rules Engine Agents (evaluating I/E criteria). Outputs are scored, ranked, and routed to a Physician Review Dashboard integrated with the CTMS (e.g., Veeva CTMS) for final validation. The system includes continuous data quality monitoring and automated explanation report generation for auditability.

Deployment follows a controlled pilot: Phase 1 builds the core data ingestion and harmonization pipeline for a single health system. Phase 2 introduces automated screening for a single protocol, with all outputs undergoing parallel manual review to validate accuracy. Phase 3 scales to federated queries and integrates with the EDC/CTMS. A critical pre-go-live step is workflow simulation using synthetic patient data to test logic and load. Governance is embedded via automated bias detection, SAE monitoring triggers, and immutable audit trails for all AI-driven eligibility flags.

This foundational agent orchestrates connections to disparate source systems (Epic, Cerner, genomic labs, etc.), extracts structured and unstructured data, and maps it to a unified clinical data model. It handles deduplication, temporal alignment, and quality validation, transforming fragmented patient records into a query-ready longitudinal profile. Without this component, data scientists and clinicians spend 60-80% of their time on manual data wrangling instead of analysis.

Specialized for clinical narratives, this agent uses a pipeline of transformer models and ontology-based rules (e.g., SNOMED CT, UMLS) to extract key phenotypes, comorbidities, medications, and outcomes from physician notes and reports. It performs entity linking to standardize terms and infers negations and family history. This turns unstructured text bloat into structured, actionable cohort criteria, replacing hundreds of hours of manual chart review per study.

The core matching intelligence. This component parses complex trial protocols into executable logic trees. It evaluates patient data against inclusion/exclusion criteria, handling temporal constraints (e.g., "diagnosis within the last 6 months, no treatment in the prior 30 days") and combinatorial logic. It outputs an eligibility score and a detailed evidence trail for each criterion, enabling transparent, auditable screening that is far more consistent than manual methods.

For multi-site or federated queries, this agent coordinates privacy-preserving matching across decentralized data environments. It manages cryptographic hashing or tokenization workflows to link patient records across institutions without moving or exposing raw PHI. It integrates with Data Use Agreement (DUA) engines to ensure each query is compliant, enabling scalable cohort discovery while maintaining strict HIPAA/GDPR adherence and institutional trust.

This control layer manages the handoff from automated screening to human oversight. It packages AI-generated eligibility recommendations with supporting evidence and routes them to the appropriate physician or study coordinator via integrated portals (e.g., EHR inbox, CTMS). It manages approval workflows, captures override reasons, and feeds decisions back into the system for learning. This ensures clinical governance and safety while freeing expert time for complex edge cases only.

A continuous governance agent that logs every data access, query, logic execution, and decision across the workflow. It generates real-time dashboards on pipeline health, cohort discovery rates, and algorithm performance (including bias monitoring). It automates the assembly of audit-ready packages for regulatory inspections, providing a defensible record of the cohort identification process. This is non-negotiable for operation in a regulated GCP environment.