A two-stage proactive dialogue generator for efficient clinical information collection using large language model

Efficient patient-doctor interaction is among the key factors for a successful disease diagnosis. During the conversation, the doctor could query complementary diagnostic information, such as the patient's symptoms, previous surgery, and other related information that goes beyond medical evidence data (test results) to enhance disease diagnosis. However, this procedure is usually time-consuming and less-efficient, which can be potentially optimized through computer-assisted systems. As such, we propose a diagnostic dialogue system to automate the patient information collection procedure. By exploiting medical history and conversation logic, our conversation agents, particularly the doctor agent, can pose multi-round clinical queries to effectively collect the most relevant disease diagnostic information. Moreover, benefiting from our two-stage recommendation structure, carefully designed ranking criteria, and interactive patient agent, our model is able to overcome the under-exploration and non-flexible challenges in dialogue generation. Our experiment results in a real-world medical conversation dataset with better performance than biomedical language model-based and Deepseek-based approaches. Our experiments show that we can generate clinical queries that mimic the conversation style of real doctors, with efficient fluency, professionalism, and safety, while effectively collecting relevant disease information.