Large language models (LLMs) are increasingly applied to outpatient referral tasks across healthcare systems. However, there is a lack of standardized evaluation criteria to assess their effectiveness, particularly in dynamic, interactive scenarios. In this study, we systematically examine the capabilities and limitations of LLMs in managing tasks within Intelligent Outpatient Referral (IOR) systems and propose a comprehensive evaluation framework specifically designed for such systems. This framework comprises two core tasks: static evaluation, which focuses on evaluating the ability of predefined outpatient referrals, and dynamic evaluation, which evaluates capabilities of refining outpatient referral recommendations through iterative dialogues. Our findings suggest that LLMs offer limited advantages over BERT-like models, but show promise in asking effective questions during interactive dialogues.

本研究针对大型语言模型（LLMs）在门诊转诊任务中的效果缺乏标准评估标准的问题，系统地考察了其在智能门诊转诊系统中的能力与局限。我们提出了一个综合评估框架，涵盖静态评估与动态评估两大核心任务，发现LLMs在互动对话中提问有效性方面展现出一定潜力。 

大型语言模型在门诊转诊中的应用：问题定义、基准测试与挑战