用大模型自动把医疗指南转成可执行流程图，方便模拟评估政策效果。
Automatic Generation of Executable BPMN Models from Medical Guidelines
- 用大模型生成带数据约束的可执行流程图，并自动修复语法错误。
- 在1000个模拟病人上测试，决策一致率超92%，复杂度越高不确定性越明显。
- 适合医疗政策制定者和数字健康系统开发者使用。
我们提出一个端到端流程，利用大语言模型（LLMs）将医疗政策文档自动转换为可执行、数据感知的业务流程模型与标注（BPMN）模型，用于基于仿真的政策评估。针对自动化政策数字化的核心挑战，本研究提出四项贡献：基于数据的BPMN生成并支持语法自动修正、可执行性增强、关键绩效指标（KPI）嵌入，以及基于熵的不确定性检测。我们在日本三个市的糖尿病肾病预防指南上评估该流程，每种后端生成100个模型，使用三种LLMs，每个模型在1,000个合成患者上执行。对于结构良好的政策，流程达到100%真实匹配，且每位患者的决策一致率完美；在所有条件下，原始决策一致性均超过92%，熵值随文档复杂度单调上升，验证了检测器能可靠区分清晰政策与需人工澄清的模糊条文。
原文摘要 · Abstract (English)
We present an end-to-end pipeline that converts healthcare policy documents into executable, data-aware Business Process Model and Notation (BPMN) models using large language models (LLMs) for simulation-based policy evaluation. We address the main challenges of automated policy digitization with four contributions: data-grounded BPMN generation with syntax auto-correction, executable augmentation, KPI instrumentation, and entropy-based uncertainty detection. We evaluate the pipeline on diabetic nephropathy prevention guidelines from three Japanese municipalities, generating 100 models per backend across three LLMs and executing each against 1,000 synthetic patients. On well-structured policies, the pipeline achieves a 100% ground-truth match with perfect per-patient decision agreement. Across all conditions, raw per-patient decision agreement exceeds 92%, and entropy scores increase monotonically with document complexity, confirming that the detector reliably separates unambiguous policies from those requiring targeted human clarification.
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