@inproceedings{qin-etal-2025-listening,
title = "Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System",
author = "Qin, Lang and
Zhang, Yao and
Liang, Hongru and
Jatowt, Adam and
Yang, Zhenglu",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.135/",
doi = "10.18653/v1/2025.findings-acl.135",
pages = "2650--2664",
ISBN = "979-8-89176-256-5",
abstract = "Medical Dialogue Systems (MDSs) have emerged as promising tools for automated healthcare support through patient-agent interactions. Previous efforts typically relied on an idealized assumption {---} patients can accurately report symptoms aligned with their actual health conditions. However, in reality, patients often misreport their symptoms, due to cognitive limitations, emotional factors, etc. Overlooking patient misreports can significantly compromise the diagnostic accuracy of MDSs. To address this critical issue, we emphasize the importance of enabling MDSs to ``listen to patients'' by tackling two key challenges: how to detect misreport and mitigate misreport effectively. In this work, we propose PaMis, a novel framework that can detect patient misreports based on calculating the structural entropy of the dialogue entity graph, and mitigate them through generating controlled clarifying questions. Our experimental results demonstrate that PaMis effectively enhances MDSs reliability by effectively addressing patient misreports during the medical response generation process."
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<abstract>Medical Dialogue Systems (MDSs) have emerged as promising tools for automated healthcare support through patient-agent interactions. Previous efforts typically relied on an idealized assumption — patients can accurately report symptoms aligned with their actual health conditions. However, in reality, patients often misreport their symptoms, due to cognitive limitations, emotional factors, etc. Overlooking patient misreports can significantly compromise the diagnostic accuracy of MDSs. To address this critical issue, we emphasize the importance of enabling MDSs to “listen to patients” by tackling two key challenges: how to detect misreport and mitigate misreport effectively. In this work, we propose PaMis, a novel framework that can detect patient misreports based on calculating the structural entropy of the dialogue entity graph, and mitigate them through generating controlled clarifying questions. Our experimental results demonstrate that PaMis effectively enhances MDSs reliability by effectively addressing patient misreports during the medical response generation process.</abstract>
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%0 Conference Proceedings
%T Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System
%A Qin, Lang
%A Zhang, Yao
%A Liang, Hongru
%A Jatowt, Adam
%A Yang, Zhenglu
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F qin-etal-2025-listening
%X Medical Dialogue Systems (MDSs) have emerged as promising tools for automated healthcare support through patient-agent interactions. Previous efforts typically relied on an idealized assumption — patients can accurately report symptoms aligned with their actual health conditions. However, in reality, patients often misreport their symptoms, due to cognitive limitations, emotional factors, etc. Overlooking patient misreports can significantly compromise the diagnostic accuracy of MDSs. To address this critical issue, we emphasize the importance of enabling MDSs to “listen to patients” by tackling two key challenges: how to detect misreport and mitigate misreport effectively. In this work, we propose PaMis, a novel framework that can detect patient misreports based on calculating the structural entropy of the dialogue entity graph, and mitigate them through generating controlled clarifying questions. Our experimental results demonstrate that PaMis effectively enhances MDSs reliability by effectively addressing patient misreports during the medical response generation process.
%R 10.18653/v1/2025.findings-acl.135
%U https://aclanthology.org/2025.findings-acl.135/
%U https://doi.org/10.18653/v1/2025.findings-acl.135
%P 2650-2664
Markdown (Informal)
[Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System](https://aclanthology.org/2025.findings-acl.135/) (Qin et al., Findings 2025)
ACL