Automatic Generation of Executable BPMN Models from Medical Guidelines

2026-04-09

AI Summary

This study addresses the challenge of automatically transforming healthcare policy documents into executable, data-aware business process models, a bottleneck in simulation-based policy evaluation. The authors propose an end-to-end pipeline that leverages large language models to convert clinical guidelines into executable BPMN (Business Process Model and Notation) models. The approach introduces four key innovations: data-driven BPMN generation, automatic syntactic correction, enhanced executability, and embedded KPIs, complemented by entropy-based uncertainty detection to flag complex clauses requiring human review. Experimental results demonstrate 100% decision consistency on well-structured guidelines and over 92% accuracy in patient-level decision modeling, confirming the method’s effectiveness and practical applicability.

Abstract

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.

Problem

• BPMN
• medical guidelines
• policy digitization
• executable models
• healthcare policy

Innovation

• executable BPMN
• large language models
• policy digitization
• entropy-based uncertainty detection
• data-aware process modeling