arXiv 2606.094892026-06-08cs.AI

LLM编排的卒中护理合规性检查无需计算机可解释指南

LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines

Computer Science Institute, DiSIT, University of Piemonte Orientale（皮埃蒙特东方大学计算机科学研究所）；Integrated Laboratory of AI and Medical Informatics, DAIRI, SS. Antonio e Biagio e Cesare Arrigo Hospital（圣安东尼奥、比亚焦与切萨雷·阿里戈医院DAIRI人工智能与医学信息学综合实验室）

Giorgio Leonardi, Stefania Montani, Manuel Striani, Alessandro Canessa, Delfina Ferrandi

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

提出基于大语言模型编排的模块化框架，从非结构化临床文本和指南中自动提取患者轨迹、识别规范规则并计算合规性指标，在卒中护理领域验证了86%以上的轨迹合规。

摘要：

目标：医疗保健中的合规性检查旨在评估患者护理路径是否符合临床指南。然而，其实际应用通常依赖于正式、机器可解释的指南表示（如计算机可解释指南CIG），而这些在现实临床环境中很少可用。方法：本文引入了一个基于大语言模型编排的模块化框架，直接从非结构化的临床和指南文本中支持医疗合规性检查，无需预定义的CIG。所提出的架构集成了多个LLM和支持组件，从临床出院信中提取患者轨迹，从文本临床指南中识别规范规则，将这些规则转换为可执行脚本，并计算轨迹合规性指标以量化事件日志中的合规性。结果：该框架在亚历山德里亚医院神经内科病房的卒中护理领域进行了实施和评估。从医院数据中自动提取了数百条患者轨迹，并根据参考指南衍生的50条规则进行了评估。分析显示，超过86%的可用轨迹是合规的。结论：结果证明了使用编排的LLM进行实际医疗保健合规性分析的可行性。同时，该研究提供了亚历山德里亚医院卒中护理指南高度遵守的证据。

英文摘要：

Objective: Conformance checking in healthcare seeks to assess whether patient care pathways adhere to clinical guidelines. However, its practical application often depends on the availability of formal, machine-interpretable representations of guidelines, such as Computer-Interpretable Guidelines (CIGs), which are seldom available in real-world clinical settings. Methods: This work introduces a modular framework based on the orchestration of Large Language Models (LLMs) to support medical conformance checking directly from unstructured clinical and guideline texts, without requiring predefined CIGs. The proposed architecture integrates multiple LLMs and supporting components to extract patient traces from clinical discharge letters, identify normative rules from textual clinical guidelines, translate these rules into executable scripts, and compute a Trace Conformance Indicator to quantify compliance within the event log. Results: The framework was implemented and evaluated in the stroke care domain at the neurological ward of Alessandria Hospital. Hundreds of patient traces were automatically extracted from hospital data and assessed against 50 rules derived from the reference guideline. The analysis showed that more than 86\% of the available traces were conformant. Conclusion: The results demonstrate the feasibility of using orchestrated LLMs for practical healthcare conformance analysis. At the same time, the study provides evidence of a high level of adherence to stroke care guidelines at Alessandria Hospital.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。