title: Exploring the Inquiry-Diagnosis Relationship with Advanced Patient
    Simulators
summary: Online medical consultation (OMC) restricts doctors to gathering patient
  information solely through inquiries, making the already complex sequential
  decision-making process of diagnosis even more challenging. Recently, the rapid
  advancement of large language models has demonstrated a significant potential
  to transform OMC. However, most studies have primarily focused on improving
  diagnostic accuracy under conditions of relatively sufficient information,
  while paying limited attention to the "inquiry" phase of the consultation
  process. This lack of focus has left the relationship between "inquiry" and
  "diagnosis" insufficiently explored. In this paper, we first extract real
  patient interaction strategies from authentic doctor-patient conversations and
  use these strategies to guide the training of a patient simulator that closely
  mirrors real-world behavior. By inputting medical records into our patient
  simulator to simulate patient responses, we conduct extensive experiments to
  explore the relationship between "inquiry" and "diagnosis" in the consultation
  process. Experimental results demonstrate that inquiry and diagnosis adhere to
  the Liebig's law: poor inquiry quality limits the effectiveness of diagnosis,
  regardless of diagnostic capability, and vice versa. Furthermore, the
  experiments reveal significant differences in the inquiry performance of
  various models. To investigate this phenomenon, we categorize the inquiry
  process into four types: (1) chief complaint inquiry; (2) specification of
  known symptoms; (3) inquiry about accompanying symptoms; and (4) gathering
  family or medical history. We analyze the distribution of inquiries across the
  four types for different models to explore the reasons behind their significant
  performance differences. We plan to open-source the weights and related code of
  our patient simulator at https://github.com/LIO-H-ZEN/PatientSimulator.
paper_authors._id: 6789f2795a84f1087bc9274a
paper_authors.name: Zhaocheng Liu
paper_authors.status: claimed_verified
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paper_authors.fullname: Zhaocheng Liu
paper_authors.type: user
paper_authors.user: zhaocheng
paper_authors._id: 6789f2795a84f1087bc9274b
paper_authors.name: Quan Tu
paper_authors._id: 6789f2795a84f1087bc9274c
paper_authors.name: Wen Ye
paper_authors.status: claimed_verified
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paper_authors.fullname: yewen
paper_authors.type: user
paper_authors.user: yw271227
paper_authors._id: 6789f2795a84f1087bc9274d
paper_authors.name: Yu Xiao
paper_authors._id: 6789f2795a84f1087bc9274e
paper_authors.name: Zhishou Zhang
paper_authors._id: 6789f2795a84f1087bc9274f
paper_authors.name: Hengfu Cui
paper_authors._id: 6789f2795a84f1087bc92750
paper_authors.name: Yalun Zhu
paper_authors._id: 6789f2795a84f1087bc92751
paper_authors.name: Qiang Ju
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-01-17T10:25:20.100Z
paper_authors._id: 62dcdb86d36b2070f928a51e
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paper_authors.fullname: sdujq
paper_authors.type: user
paper_authors.user: sdujq
paper_authors._id: 6789f2795a84f1087bc92752
paper_authors.name: Shizheng Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:34:35.155Z
paper_authors._id: 64ab92c362b769f936bba203
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/64ab92c362b769f936bba203/Kq3Nlnq3DTPwungx8r9G5.jpeg
paper_authors.fullname: Shizheng Li
paper_authors.type: user
paper_authors.user: ShizhengLi
paper_authors._id: 6789f2795a84f1087bc92753
paper_authors.name: Jian Xie
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-01-17T10:34:48.855Z
paper_authors._id: 62d65139667051e0a29bffe7
paper_authors.avatarUrl: /avatars/0252aa2bcd4cf1c8e4b87e5f164b6da5.svg
paper_authors.fullname: Jian Xie
paper_authors.type: user
paper_authors.user: hsaest
paper_title: Exploring the Inquiry-Diagnosis Relationship with Advanced Patient
    Simulators
paper_summary: Online medical consultation (OMC) restricts doctors to gathering patient
  information solely through inquiries, making the already complex sequential
  decision-making process of diagnosis even more challenging. Recently, the rapid
  advancement of large language models has demonstrated a significant potential
  to transform OMC. However, most studies have primarily focused on improving
  diagnostic accuracy under conditions of relatively sufficient information,
  while paying limited attention to the "inquiry" phase of the consultation
  process. This lack of focus has left the relationship between "inquiry" and
  "diagnosis" insufficiently explored. In this paper, we first extract real
  patient interaction strategies from authentic doctor-patient conversations and
  use these strategies to guide the training of a patient simulator that closely
  mirrors real-world behavior. By inputting medical records into our patient
  simulator to simulate patient responses, we conduct extensive experiments to
  explore the relationship between "inquiry" and "diagnosis" in the consultation
  process. Experimental results demonstrate that inquiry and diagnosis adhere to
  the Liebig's law: poor inquiry quality limits the effectiveness of diagnosis,
  regardless of diagnostic capability, and vice versa. Furthermore, the
  experiments reveal significant differences in the inquiry performance of
  various models. To investigate this phenomenon, we categorize the inquiry
  process into four types: (1) chief complaint inquiry; (2) specification of
  known symptoms; (3) inquiry about accompanying symptoms; and (4) gathering
  family or medical history. We analyze the distribution of inquiries across the
  four types for different models to explore the reasons behind their significant
  performance differences. We plan to open-source the weights and related code of
  our patient simulator at https://github.com/LIO-H-ZEN/PatientSimulator.