Exploring the Inquiry-Diagnosis Relationship with Advanced Patient
  Simulators
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.
6789f2795a84f1087bc9274a
Zhaocheng Liu
claimed_verified
2025-01-17T10:25:21.951Z
633e570be7d5ce7bfe037a53
https://cdn-avatars.huggingface.co/v1/production/uploads/633e570be7d5ce7bfe037a53/zV8ULv4Mu7YIGZ8D3JtmK.jpeg
Zhaocheng Liu
user
zhaocheng
6789f2795a84f1087bc9274b
Quan Tu
6789f2795a84f1087bc9274c
Wen Ye
claimed_verified
2025-01-20T09:29:03.948Z
670bd33ec9c7e01d3dbaf59d
https://cdn-avatars.huggingface.co/v1/production/uploads/noauth/PCL5O0f3DXjKrc_EvdAgb.png
yewen
user
yw271227
6789f2795a84f1087bc9274d
Yu Xiao
6789f2795a84f1087bc9274e
Zhishou Zhang
6789f2795a84f1087bc9274f
Hengfu Cui
6789f2795a84f1087bc92750
Yalun Zhu
6789f2795a84f1087bc92751
Qiang Ju
claimed_verified
2025-01-17T10:25:20.100Z
62dcdb86d36b2070f928a51e
/avatars/a341e4305217f8abd14cff97201a24aa.svg
sdujq
user
sdujq
6789f2795a84f1087bc92752
Shizheng Li
admin_assigned
2025-01-17T10:34:35.155Z
64ab92c362b769f936bba203
https://cdn-avatars.huggingface.co/v1/production/uploads/64ab92c362b769f936bba203/Kq3Nlnq3DTPwungx8r9G5.jpeg
Shizheng Li
user
ShizhengLi
6789f2795a84f1087bc92753
Jian Xie
admin_assigned
2025-01-17T10:34:48.855Z
62d65139667051e0a29bffe7
/avatars/0252aa2bcd4cf1c8e4b87e5f164b6da5.svg
Jian Xie
user
hsaest
Exploring the Inquiry-Diagnosis Relationship with Advanced Patient
  Simulators
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.
Experiments with a patient simulator show that the quality of the inquiry phase significantly affects diagnostic effectiveness in online medical consultations.
patient simulator Liebig's law inquiry types chief complaint inquiry specification of known symptoms inquiry about accompanying symptoms gathering family or medical history