Rare Disease Patient Finding Across Decentralized Networks

Manual searches across decentralized clinics and registries can take 18-24 months for ultra-rare conditions. A custom orchestration workflow automates federated queries, natural language phenotyping, and record linkage, compressing initial candidate identification to a 4-8 week cycle. This acceleration de-risks trial timelines and creates a predictable recruitment pipeline for sponsors.

Traditional patient finding relies on expensive site grants, CRO outreach, and manual chart reviews. An automated workflow replaces these variable costs with a fixed, scalable architecture. Savings come from eliminating redundant manual searches, reducing site feasibility contracts for 'maybe' candidates, and leveraging existing EHR and registry infrastructure without new per-query fees.

Manual screening often yields low-quality leads, wasting site resources on ineligible patients. A custom workflow applies protocol-specific logic, temporal reasoning across longitudinal records, and genotype-phenotype correlation to pre-rank candidates by match confidence. This precision directly improves site efficiency, conserves patient goodwill, and increases the percentage of identified patients who successfully enroll.

A one-off search doesn't scale. A production workflow architecture—built with tools like LangGraph for agent orchestration and privacy-preserving record linkage (PPRL) techniques—creates a repeatable operating model. This allows sponsors to run parallel searches across multiple rare disease programs or geographies using the same core system, turning patient finding from a bespoke service into a scalable capability.

Manual data sharing and ad-hoc queries create compliance exposure. A governed workflow embeds Data Use Agreement (DUA) checks, consent validation, and audit logging into every query step. Automated compliance monitoring for HIPAA/GDPR and synthetic data generation for feasibility testing provide defensible controls, reducing legal risk and accelerating IRB approvals for the actual study.

Uncertain patient prevalence leads to overly broad protocols and costly amendments. By running simulated queries against real-world data (via synthetic cohorts or privacy-preserving techniques), the workflow generates data-driven prevalence estimates. This allows for realistic protocol optimization before finalization, reducing mid-study amendments and improving the overall probability of trial success.

The central agent that coordinates privacy-preserving searches across decentralized hospital networks, biobanks, and registries without moving patient data. It uses Privacy-Preserving Record Linkage (PPRL) techniques and tokenization to execute protocol-specific queries, aggregate anonymized counts, and route high-probability matches for deeper review. This component eliminates the manual, institution-by-institution outreach that delays rare disease studies by months or years.

A specialized NLP agent that processes unstructured clinical notes, discharge summaries, and genetic counseling reports to identify patients matching complex, often descriptive, rare disease phenotypes. It uses ontology-driven entity linking (e.g., HPO, SNOMED CT) and temporal reasoning to extract symptoms, family history, and disease progression from narrative bloat, creating structured candidate profiles for the matching engine.

The core logic layer that evaluates candidate patient profiles against dozens of open trial protocols simultaneously. It scores matches based on inclusion/exclusion criteria fit, biomarker status, geographic feasibility, and predicted enrollment likelihood. The engine outputs a dynamically ranked list, enabling sites and sponsors to prioritize the highest-value candidates across multiple studies, maximizing recruitment efficiency.

An orchestration agent that manages the compliant, personalized outreach sequence once a potential match is identified and approved. It generates tailored communication for treating physicians, triggers pre-screening packets to sites, and can initiate guided, digital consent workflows with patients/caregivers. This component replaces fragmented email and phone tag, creating a traceable, scalable enrollment pathway.

A policy enforcement agent that dynamically governs all data access and patient contact based on real-time consent stipulations, Data Use Agreements (DUAs), and IRB approvals. It sits between the matching engine and outreach modules, ensuring every action is logged, auditable, and compliant. This is critical for maintaining trust in decentralized networks and providing defensible audit trails for regulators.

A pre-production agent that generates statistically realistic synthetic patient datasets to model cohort size and characteristics before querying real Protected Health Information (PHI). It allows sponsors to stress-test protocol criteria and predict enrollment curves in a risk-free environment, enabling data-driven protocol optimization and de-risking feasibility assessments.