Exploring the Inquiry-Diagnosis Relationship with Advanced Patient
  Simulators
Authors: name: Zhaocheng Liu; name: Quan Tu; name: Wen Ye; name: Yu Xiao; name: Zhishou Zhang; name: Hengfu Cui; name: Yalun Zhu; name: Qiang Ju; name: Shizheng Li; name: Jian Xie
Published: 2025-01-16 11:41:14

Abstract: 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.