用大模型从原始文本自动检查脑卒中治疗是否符合指南，无需机器可读的规则
LLM-Orchestrated Conformance Checking in Stroke Care Without Computer-Interpretable Guidelines
- 通过多模型协作，从病历和指南文本中提取诊疗流程与规范条目
- 在亚历山德里亚医院测试中，86%以上患者路径符合50条指南规则
- 适合无标准化指南数据的医疗机构做临床质量评估
目的：医疗合规性检查旨在评估患者诊疗路径是否符合临床指南。然而，其实际应用常依赖于计算机可读的指南（CIGs），这类形式化表达在真实临床环境中极为罕见。方法：本文提出一种基于大型语言模型（LLMs）编排的模块化框架，可直接从非结构化临床记录和指南文本中实现合规性检查，无需预定义的CIGs。该架构整合多个LLMs及辅助组件，从出院小结中提取患者诊疗轨迹，从文本指南中识别规范规则，将规则转化为可执行脚本，并计算事件日志中的轨迹合规性指标。结果：该框架在亚历山德里亚医院神经内科脑卒中领域实现部署。从医院数据中自动提取了数百条患者轨迹，并依据参考指南推导出50条规则进行评估。分析显示，超过86%的可用轨迹符合指南要求。结论：研究证明了采用编排式大模型进行实际医疗合规性分析的可行性，同时表明亚历山德里亚医院在脑卒中治疗方面具有较高指南遵循水平。
原文摘要 · Abstract (English)
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.
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