CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment

• URL: http://arxiv.org/abs/2512.10206v2
• Date: Fri, 12 Dec 2025 01:38:55 GMT
• Title: CP-Env: Evaluating Large Language Models on Clinical Pathways in a Controllable Hospital Environment
• Authors: Yakun Zhu, Zhongzhen Huang, Qianhan Feng, Linjie Mu, Yannian Gu, Shaoting Zhang, Qi Dou, Xiaofan Zhang,
• Abstract summary: We introduce CP-Env, a controllable agentic hospital environment designed to evaluate large language models (LLMs) across end-to-end clinical pathways.<n>Following real hospital adaptive flow of healthcare, it enables branching, long-horizon task execution.<n>Results reveal that most models struggle with pathway hallucinations, exhibiting complexity and losing critical diagnostic details.
• Score: 29.48544328813161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Medical care follows complex clinical pathways that extend beyond isolated physician-patient encounters, emphasizing decision-making and transitions between different stages. Current benchmarks focusing on static exams or isolated dialogues inadequately evaluate large language models (LLMs) in dynamic clinical scenarios. We introduce CP-Env, a controllable agentic hospital environment designed to evaluate LLMs across end-to-end clinical pathways. CP-Env simulates a hospital ecosystem with patient and physician agents, constructing scenarios ranging from triage and specialist consultation to diagnostic testing and multidisciplinary team meetings for agent interaction. Following real hospital adaptive flow of healthcare, it enables branching, long-horizon task execution. We propose a three-tiered evaluation framework encompassing Clinical Efficacy, Process Competency, and Professional Ethics. Results reveal that most models struggle with pathway complexity, exhibiting hallucinations and losing critical diagnostic details. Interestingly, excessive reasoning steps can sometimes prove counterproductive, while top models tend to exhibit reduced tool dependency through internalized knowledge. CP-Env advances medical AI agents development through comprehensive end-to-end clinical evaluation. We provide the benchmark and evaluation tools for further research and development at https://github.com/SPIRAL-MED/CP_ENV.

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