构建符合HIPAA的AI增强型实验室系统，实现临床检测全流程可视化与自动化。
FMRP-LEAN: A HIPAA-Compliant AI-Augmented LIMS Architecture for End-to-End Clinical Assay Workflow Optimization
- 用有限状态机建模样本生命周期，支持可追踪的流程控制。
- 部署后检测报告延迟降低，跨团队协作透明度显著提升。
- 适合医疗科研中需合规数据管理的多日复杂检测场景。
转化研究中的临床生物标志物流程常依赖电子表格追踪、人工质控核对及松散集成系统，导致状态可见性差、报告延迟和操作风险高。此类问题在多日检测（如基于Luminex的脆性X综合征信使核糖核蛋白FMRP定量）中尤为突出，需满足HIPAA合规的数据治理、确定性工作流推进及实验室与临床团队间的协调沟通。本文提出FMRP-LEAN，一种符合HIPAA标准的AI增强型实验室信息管理系统（LIMS）架构，通过带显式转换条件与停留时间可观测性的有限状态工作流模型，形式化样本全生命周期管理。系统采用自托管Supabase/PostgreSQL栈部署于医院控制的基础设施内，结合混合边缘-内部隔离、加密隧道与仅回环服务，并实现与REDCap双向同步。统一的MRN-UUIDv7标识框架配合二维码追踪，在保留患者身份信息的前提下确保临床-研究关联可追溯。FMRP-LEAN集成自动统计质控预筛查与受治理约束的AI操作模块，仅在聚合预测结果上运行，具备确定性回退保障。部署验证显示工作流可观测性提升，质控延迟减少，实验室技术人员、研究协调员与面向患者的团队间透明度增强。该架构为受监管医疗环境下的安全、状态明确且AI辅助的临床研究工作流提供了可复现范式。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。