  Online healthcare consultation in virtual health is an emerging industry
marked by innovation and fierce competition. Accurate and timely prediction of
healthcare consultation success can proactively help online platforms address
patient concerns and improve retention rates. However, predicting online
consultation success is challenging due to the partial role of virtual
consultations in patients' overall healthcare journey and the disconnect
between online and in-person healthcare IT systems. Patient data in online
consultations is often sparse and incomplete, presenting significant technical
challenges and a research gap. To address these issues, we propose the Dynamic
Knowledge Network and Multimodal Data Fusion (DyKoNeM) framework, 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.
