FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization
2026-07-22 • Human-Computer Interaction
Human-Computer InteractionArtificial IntelligenceEmerging Technologies
AI summaryⓘ
The authors created FMRP-LEAN, a secure computer system that helps track and manage lab samples used in research on Fragile X protein. It replaces messy spreadsheets with a clear, step-by-step workflow that tracks each sample’s status and ensures data privacy following hospital rules. Their system uses AI to check data quality automatically and links clinical and research information safely. When tested, it made lab work faster, clearer, and more coordinated between different teams. This approach offers a new way to run clinical research workflows securely and efficiently in hospitals.
FMRPLIMSHIPAAbiomarker workflowfinite-state workflowQC (Quality Control)REDCapSupabaseUUIDAI operations
Authors
Eva McCord, Ernest Pedapati, Zag ElSayed
Abstract
Clinical biomarker workflows in translational research settings often rely on spreadsheet-driven tracking, manual quality control (QC) reconciliation, and loosely integrated systems, resulting in limited state visibility, delayed reporting, and increased operational risk. These challenges are particularly pronounced in multi-day assays such as Luminex-based quantification of Fragile X Messenger Ribonucleoprotein (FMRP), where HIPAA-compliant data governance, deterministic workflow progression, and coordinated communication across laboratory and clinical teams are required. This paper presents FMRP-LEAN, a HIPAA-compliant, AI-augmented Laboratory Information Management System (LIMS) architecture that formalizes biospecimen lifecycle management through a finite-state workflow model with explicit transition guards and dwell-time observability. The system integrates a self-hosted Supabase/PostgreSQL stack deployed within hospital-controlled infrastructure, hybrid edge-internal isolation with encrypted tunneling and loopback-only services, and bi-directional REDCap synchronization. A unified MRN-UUIDv7 identifier framework with QR-based tracking ensures traceable clinical-research linkage under PHI residency constraints. FMRP-LEAN incorporates automated statistical QC pre-screening and a governance-constrained AI operations module that operates exclusively on aggregate projections, with deterministic fallback guarantees. Deployment demonstrates improved workflow observability, reduced QC latency, and enhanced cross-role transparency between laboratory technicians, research coordinators, and patient-facing teams. The architecture provides a reproducible model for secure, state-explicit, and AI-augmented clinical research workflows in regulated healthcare environments.