@inproceedings{hsu-etal-2025-medplan,
title = "{M}ed{P}lan: A Two-Stage {RAG}-Based System for Personalized Medical Plan Generation",
author = "Hsu, Hsin-Ling and
Dao, Cong-Tinh and
Wang, Luning and
Shuai, Zitao and
Phan, Thao Nguyen Minh and
Ding, Jun-En and
Liao, Chun-Chieh and
Hu, Pengfei and
Han, Xiaoxue and
Hsu, Chih-Ho and
Luo, Dongsheng and
Peng, Wen-Chih and
Liu, Feng and
Hung, Fang-Ming and
Wu, Chenwei",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.76/",
doi = "10.18653/v1/2025.acl-industry.76",
pages = "1072--1082",
ISBN = "979-8-89176-288-6",
abstract = "Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We identify three critical limitations in current approaches: they generate treatment plans in a single pass rather than following the sequential reasoning process used by clinicians; they rarely incorporate patient-specific historical context; and they fail to effectively distinguish between subjective and objective clinical information. Motivated by the SOAP methodology (Subjective, Objective, Assessment, Plan), we introduce MedPlan, a novel framework that structures LLM reasoning to align with real-life clinician workflows. Our approach employs a two-stage architecture that first generates a clinical assessment based on patient symptoms and objective data, then formulates a structured treatment plan informed by this assessment and enriched with patient-specific information through retrieval-augmented generation. Comprehensive evaluation demonstrates that our method significantly outperforms baseline approaches in both assessment accuracy and treatment plan quality. Our demo system and code are available at https://github.com/JustinHsu1019/MedPlan."
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<abstract>Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We identify three critical limitations in current approaches: they generate treatment plans in a single pass rather than following the sequential reasoning process used by clinicians; they rarely incorporate patient-specific historical context; and they fail to effectively distinguish between subjective and objective clinical information. Motivated by the SOAP methodology (Subjective, Objective, Assessment, Plan), we introduce MedPlan, a novel framework that structures LLM reasoning to align with real-life clinician workflows. Our approach employs a two-stage architecture that first generates a clinical assessment based on patient symptoms and objective data, then formulates a structured treatment plan informed by this assessment and enriched with patient-specific information through retrieval-augmented generation. Comprehensive evaluation demonstrates that our method significantly outperforms baseline approaches in both assessment accuracy and treatment plan quality. Our demo system and code are available at https://github.com/JustinHsu1019/MedPlan.</abstract>
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%0 Conference Proceedings
%T MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation
%A Hsu, Hsin-Ling
%A Dao, Cong-Tinh
%A Wang, Luning
%A Shuai, Zitao
%A Phan, Thao Nguyen Minh
%A Ding, Jun-En
%A Liao, Chun-Chieh
%A Hu, Pengfei
%A Han, Xiaoxue
%A Hsu, Chih-Ho
%A Luo, Dongsheng
%A Peng, Wen-Chih
%A Liu, Feng
%A Hung, Fang-Ming
%A Wu, Chenwei
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F hsu-etal-2025-medplan
%X Despite recent success in applying large language models (LLMs) to electronic health records (EHR), most systems focus primarily on assessment rather than treatment planning. We identify three critical limitations in current approaches: they generate treatment plans in a single pass rather than following the sequential reasoning process used by clinicians; they rarely incorporate patient-specific historical context; and they fail to effectively distinguish between subjective and objective clinical information. Motivated by the SOAP methodology (Subjective, Objective, Assessment, Plan), we introduce MedPlan, a novel framework that structures LLM reasoning to align with real-life clinician workflows. Our approach employs a two-stage architecture that first generates a clinical assessment based on patient symptoms and objective data, then formulates a structured treatment plan informed by this assessment and enriched with patient-specific information through retrieval-augmented generation. Comprehensive evaluation demonstrates that our method significantly outperforms baseline approaches in both assessment accuracy and treatment plan quality. Our demo system and code are available at https://github.com/JustinHsu1019/MedPlan.
%R 10.18653/v1/2025.acl-industry.76
%U https://aclanthology.org/2025.acl-industry.76/
%U https://doi.org/10.18653/v1/2025.acl-industry.76
%P 1072-1082
Markdown (Informal)
[MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation](https://aclanthology.org/2025.acl-industry.76/) (Hsu et al., ACL 2025)
ACL
- Hsin-Ling Hsu, Cong-Tinh Dao, Luning Wang, Zitao Shuai, Thao Nguyen Minh Phan, Jun-En Ding, Chun-Chieh Liao, Pengfei Hu, Xiaoxue Han, Chih-Ho Hsu, Dongsheng Luo, Wen-Chih Peng, Feng Liu, Fang-Ming Hung, and Chenwei Wu. 2025. MedPlan: A Two-Stage RAG-Based System for Personalized Medical Plan Generation. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 1072–1082, Vienna, Austria. Association for Computational Linguistics.