用大模型模拟医患对话，提升门诊接待效率与个性化服务
PIORS: Personalized Intelligent Outpatient Reception based on Large Language Model with Multi-Agents Medical Scenario Simulation
- 构建多智能体系统，结合大模型与医院信息平台实现真实场景接待
- 在15名用户和专家评估中，性能超越GPT-4o，符合临床需求
- 提出医疗对话生成框架SFMSS，提升模型对真实医疗流程的适应性
在中国，门诊接待护士面临繁重工作量，影响每位患者的沟通质量与服务体验。本文提出个性化智能门诊接待系统（PIORS），将基于大语言模型的虚拟护士与医院信息系统（HIS）协同集成于真实门诊场景，旨在提供高效、个性化的接待服务。为提升大模型在真实医疗环境中的表现，我们设计了面向服务流程的医疗情景仿真（SFMSS）数据生成框架，以增强模型对实际医疗流程的理解。通过15名用户和15名临床专家的自动与人工评估验证，结果表明PIORS-Nurse在多项指标上优于现有基线模型，包括当前最先进的GPT-4o，且更契合人类偏好与临床需求。更多细节与演示见https://github.com/FudanDISC/PIORS。
原文摘要 · Abstract (English)
In China, receptionist nurses face overwhelming workloads in outpatient settings, limiting their time and attention for each patient and ultimately reducing service quality. In this paper, we present the Personalized Intelligent Outpatient Reception System (PIORS). This system integrates an LLM-based reception nurse and a collaboration between LLM and hospital information system (HIS) into real outpatient reception setting, aiming to deliver personalized, high-quality, and efficient reception services. Additionally, to enhance the performance of LLMs in real-world healthcare scenarios, we propose a medical conversational data generation framework named Service Flow aware Medical Scenario Simulation (SFMSS), aiming to adapt the LLM to the real-world environments and PIORS settings. We evaluate the effectiveness of PIORS and SFMSS through automatic and human assessments involving 15 users and 15 clinical experts. The results demonstrate that PIORS-Nurse outperforms all baselines, including the current state-of-the-art model GPT-4o, and aligns with human preferences and clinical needs. Further details and demo can be found at https://github.com/FudanDISC/PIORS
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