Paper Overview
- Field: Machine Learning
- Authors: Eva McCord, Ernest Pedapati, Zag ElSayed
- Published: 2026-07-24
- arXiv: 2507.18396
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 bidirectional REDCap synchronization.
A unified MRN-UUIDv7 identifier framework with QR-based tracking ensures traceable clinical research linkage under PHI residency constraints. FMRP-LEAN includes automated statistical QC pre-screening and a governance-constrained AI operations module that operates only on aggregated projections with deterministic fallback guarantees. Deployment demonstrated improved workflow observability, reduced QC latency, and enhanced cross-role transparency among laboratory technicians, research coordinators, and patient-facing teams. The architecture provides a replicable model for secure, state-explicit, AI-augmented clinical research workflows in regulated healthcare environments.
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*Auto-collected on 2026-07-24*