Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System

ACL ARR 2025 February Submission4824 Authors

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.

Paper Type: Long

Research Area: NLP Applications

Research Area Keywords: healthcare applications, knowledge graphs

Languages Studied: Chinese, English

Submission Number: 4824