Immunotherapy Trial Candidate Immune Profile Screening

Manual review of EHRs, flow cytometry reports, and lab histories for immune markers (e.g., lymphocyte subsets, PD-L1 status, prior immunotherapy response) takes 45-90 minutes per patient. A custom multi-agent workflow using NLP and structured data queries can screen 100+ records per hour, compressing weeks of coordinator work into days and allowing sites to identify and contact candidates before competing trials enroll them.

Manual screening is error-prone for complex, multi-parameter immune criteria (e.g., 'absolute lymphocyte count >500 cells/μL within 30 days prior to screening, with no active autoimmune disease'). An orchestrated workflow applies temporal logic and ontology-based rules consistently, flagging borderline cases for human review. This reduces screen failures and ensures enrolled patients are scientifically optimal, protecting trial integrity and statistical power.

Enrollment delays are the single largest cost driver in clinical development. Accelerating patient identification for high-value immunotherapy trials directly reduces site monitoring burn, idle drug supply, and program overhead. The workflow's architecture—integrating with Epic/Cerner, lab middleware, and flow cytometry databases—turns a fixed, manual cost center into a scalable, variable-cost capability, improving trial economics from feasibility through to database lock.

Static screening cannot support modern trial designs that require continuous re-evaluation of patients against evolving biomarker arms. This custom workflow acts as a real-time matching engine, re-screening the patient pool as new arms open or biomarker definitions refine. The architecture uses event-driven triggers (new lab result, updated pathology) and a persistent patient profile to enable dynamic cohort management, turning patient finding from a one-time event into a continuous operational capability.

Automated eligibility decisions must be explainable. This workflow architecture embeds evidence capture, linking each 'flag' or 'exclude' decision to the source data point (e.g., specific lab value, date, note excerpt) and the applied protocol rule. Automated report generation for monitor visits or FDA audits turns a compliance burden into a byproduct of operations, reducing pre-inspection preparation from weeks to hours and strengthening regulatory confidence.

Manual processes limit patient finding to a site's immediate EHR. This workflow is designed for federated or centralized deployment, enabling a sponsor or CRO to run identical screening logic across dozens of sites' EHRs (via FHIR APIs) or centralized data warehouses. The architecture handles data normalization and privacy-preserving record linkage, transforming a fragmented, site-dependent process into a unified, network-level recruitment engine that maximizes pool size for rare immune phenotypes.

Primary Goal: Reduce enrollment timelines and improve candidate quality for high-cost immunotherapy trials. They fund the build to solve the manual bottleneck of reviewing flow cytometry, CBC differentials, and prior immunotherapy history across fragmented EHR and lab systems.

Key Concerns: Protocol adherence, screening accuracy, auditability, and seamless integration with existing CTMS and EDC platforms. They measure success in weeks saved per site activation and percentage increase in screen-pass rate.

Primary Goal: Architect a robust, scalable data pipeline. They design the agents for ingesting and harmonizing HL7/FHIR streams from EHRs (Epic, Cerner), structured lab data (LIS), and unstructured pathology/flow cytometry reports.

Key Deliverables: A containerized orchestration layer (e.g., LangGraph, Apache Airflow) that normalizes immune parameters (e.g., absolute lymphocyte count, CD4+/CD8+ ratios), applies temporal logic for prior treatment history, and outputs structured candidate profiles. They own the data model and API contracts.

Primary Goal: Ensure clinical validity and safety. They define the complex medical logic (e.g., "autoimmune disease history, but not if resolved >5 years ago") and approve the rule sets that the AI agents will execute.

Key Role: They participate in the build phase to create a human-in-the-loop review interface for flagged candidates and ambiguous cases. Their sign-off is required before the workflow goes live, and they govern ongoing protocol amendments.

Primary Goal: Enforce HIPAA/GDPR and institutional data governance. They mandate the privacy-preserving architecture, which may include tokenization for record linkage and strict access controls for PHI.

Key Controls: They require automated audit trails for every patient record queried, justification for eligibility flags, and compliance checks against Data Use Agreements (DUAs). They block deployment until the workflow passes security review and data egress controls are validated.

Primary Goal: Reduce administrative burden and manual chart review. They interact with the output: a prioritized, pre-screened list of potential candidates with clear evidence summaries (e.g., "Labs from 2024-03-15 show CD19+ = 250 cells/μL").

Key Need: Intuitive dashboards embedded within or connected to their familiar EHR and CTMS workspaces. The workflow must include a simple mechanism for them to confirm findings, add local context, and trigger the formal screening visit. Adoption hinges on time saved per candidate.

Primary Goal: Manage contracts, SLAs, and integration with external data providers. They handle the commercial agreements for accessing third-party genomic databases, specialized flow cytometry analytics services, or federated network queries.

Key Function: They structure the engagement model with the automation vendor (e.g., Inference Systems), ensuring the build includes clear milestones for pilot, validation, and scale-out phases. They tie payment to measurable throughput improvements and system uptime.