LLM在动态问诊中比传统模型更擅长精准转诊。
Do LLMs Triage Like Clinicians? A Dynamic Study of Outpatient Referral
- 将转诊建模为多轮对话过程，通过追问减少不确定性
- 动态场景下LLM转诊准确率显著优于静态分类模型
- 适合需要交互式决策支持的临床辅助系统开发者
门诊转诊是核心临床流程，需在信息不全且动态变化的情况下将患者分配至相应科室，但现有研究常将其简化为静态分类任务。本文将转诊视为由信息获取与不确定性降低驱动的动态过程，对比基于固定信息的静态场景与包含多轮对话的动态场景。结果表明，LLM在静态转诊中的表现与传统分类器相当，但在动态场景中能通过提出具有判别力的追问问题，有效降低候选科室的不确定性，显著提升转诊效果。这说明LLM的核心价值并非静态预测，而在于支持交互式、以不确定性为导向的临床决策。
原文摘要 · Abstract (English)
Outpatient referral (OR) is a core clinical workflow that assigns patients to hospital departments under incomplete and evolving information, yet it is commonly simplified as a static classification problem despite being inherently interactive in practice. In this work, we study outpatient referral as a dynamic process driven by information acquisition and uncertainty reduction. We analyze both static scenarios based on fixed patient information and dynamic scenarios involving multi-turn dialogue, to test whether large language models (LLMs) improve referral outcomes through better prediction or more effective questioning. Our findings show that LLMs offer limited advantages over traditional classifiers in static referral accuracy, but consistently outperform them in dynamic settings by asking discriminative follow-up questions that reduce uncertainty over candidate departments. These results suggest that the primary value of LLMs in outpatient referral lies not in static prediction, but in supporting interactive, uncertainty-aware clinical decision-making.
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