Automatic Generation of Executable BPMN Models from Medical Guidelines
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918436452433920 |
|---|---|
| author | Sekar, Praveen Kumar Menaka Matei, Ion Zhenirovskyy, Maksym Wong, Hon Yung Kohmura, Sayuri Hotta, Shinji Inomata, Akihiro |
| author_facet | Sekar, Praveen Kumar Menaka Matei, Ion Zhenirovskyy, Maksym Wong, Hon Yung Kohmura, Sayuri Hotta, Shinji Inomata, Akihiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_07817 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Automatic Generation of Executable BPMN Models from Medical Guidelines Sekar, Praveen Kumar Menaka Matei, Ion Zhenirovskyy, Maksym Wong, Hon Yung Kohmura, Sayuri Hotta, Shinji Inomata, Akihiro Artificial Intelligence Machine Learning Software Engineering 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. |
| title | Automatic Generation of Executable BPMN Models from Medical Guidelines |
| topic | Artificial Intelligence Machine Learning Software Engineering |
| url | https://arxiv.org/abs/2604.07817 |