Decoding Virtual Healthcare Success through Knowledge-Aware and Multimodal Predictive Modeling

TL;DR

This paper presents a novel multimodal and knowledge-aware predictive model for online healthcare consultation success, addressing data sparsity and fragmentation issues to improve prediction accuracy and interpretability.

Contribution

It introduces a dynamic knowledge network fusion approach that integrates heterogeneous data sources for better prediction of consultation outcomes.

Findings

Enhanced prediction accuracy over baseline models

Improved interpretability of consultation success factors

Demonstrated effectiveness on real-world online healthcare data

Abstract

Online healthcare consultations have transformed how patients seek medical advice, offering convenience while introducing new challenges for ensuring consultation success. Predicting whether an online consultation will be successful is critical for improving patient experiences and sustaining platform competitiveness. Yet, such prediction is inherently difficult due to the fragmented nature of patients' care journeys and the lack of integration between virtual and traditional healthcare systems. Furthermore, the data collected from online platforms, including textual conversations, interaction sequences, and behavioral traces, are often sparse and incomplete. This study develops a predictive modeling approach that fuses multimodal data and dynamically constructed knowledge networks to capture latent relationships among patients, physicians, and consultation contexts. By integrating heterogeneous information sources and uncovering the evolving structure of digital interactions, the model enhances the accuracy and interpretability of consultation success prediction. The findings offer implications for designing hybrid healthcare ecosystems that combine online and offline services through data-driven intelligence.