Balancing Knowledge Delivery and Emotional Comfort in Healthcare Conversational Systems

TL;DR

This paper develops a method to improve healthcare dialogue systems by balancing medical knowledge delivery with emotional support, leading to more reassuring patient interactions.

Contribution

It introduces a data augmentation and fine-tuning approach that enhances large language models to generate both accurate medical information and empathetic responses.

Findings

Enhanced emotional response generation in models

Maintained accuracy of medical knowledge

Significant improvement over baseline models

Abstract

With the advancement of large language models, many dialogue systems are now capable of providing reasonable and informative responses to patients' medical conditions. However, when patients consult their doctor, they may experience negative emotions due to the severity and urgency of their situation. If the model can provide appropriate comfort and empathy based on the patient's negative emotions while answering medical questions, it will likely offer a more reassuring experience during the medical consultation process. To address this issue, our paper explores the balance between knowledge sharing and emotional support in the healthcare dialogue process. We utilize a large language model to rewrite a real-world interactive medical dialogue dataset, generating patient queries with negative emotions and corresponding medical responses aimed at soothing the patient's emotions while addressing their concerns. The modified data serves to refine the latest large language models with various fine-tuning methods, enabling them to accurately provide sentences with both emotional reassurance and constructive suggestions in response to patients' questions. Compared to the original LLM model, our experimental results demonstrate that our methodology significantly enhances the model's ability to generate emotional responses while maintaining its original capability to provide accurate knowledge-based answers.