这篇论文讲FMRP-LEAN架构，用有限状态机加AI来理顺临床检测流程，特别适合需要HIPAA合规的医疗数据环境。
FMRP-LEAN是一种基于有限状态工作流模型的HIPAA合规AI增强LIMS架构，用于优化FMRP蛋白定量等临床检测流程。系统采用自托管Supabase/PostgreSQL栈部署在医院控制的基础设施内，支持混合边缘-内部隔离和加密隧道。它整合了统一MRN-UUIDv7标识符框架和QR追踪，确保受保护健康信息(PHI)下的临床-研究可追溯性。架构包含自动化统计QC预筛选和受治理约束的AI操作模块，仅在聚合投影上运行并提供确定性回退保障。部署结果表明工作流可观测性提升，QC延迟降低，跨角色透明度增强。
FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization
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.