Predicting Consultation Success in Online Health Platforms Using Dynamic Knowledge Networks and Multimodal Data Fusion

Online healthcare consultation in virtual health is an emerging industry marked by innovation and fierce competition. Accurate and early prediction of healthcare consultation success can help online platforms proactively address patient concerns and improve retention rates. However, this prediction task is inherently challenging due to several factors: patients’ needs often remain unclear until they explicitly articulate them, and their questions may evolve throughout the consultation process. Additionally, the task involves processing multimodal input information, including consultation dialogues and the complex network of various stakeholders in a patient’s healthcare journey. To address these issues, we propose the Dynamic Knowledge Network and Multimodal Data Fusion framework with a dynamic knowledge graph and multimodal data fusion, which enhances the predictive power of online healthcare consultations. Our work has important implications for new business models where specific and detailed online communication processes are stored in the IT database, and at the same time, latent information with predictive power is embedded in the network formed by stakeholders’ digital traces. It can be extended to diverse industries and domains, where the virtual or hybrid model (e.g., integration of online and offline services) is emerging as a prevailing trend.

• 저널MIS Quarterly · 1–32
• 토픽Machine Learning in Healthcare · Artificial Intelligence
• DOI10.25300/misq/2025/19444
• 저자Shuang Geng, Wenli Zhang, Jiaheng Xie, Gemin Liang, Ben Niu, Sudha Ram