Jiaheng Xie

Shuang Geng, Wenli Zhang, Jiaheng Xie et al.

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.

Jiaheng Xie, Xiao Liu

Predicting video viewership is a top priority for content creators and video-sharing sites. Content creators live on such predictions to maximize influences and minimize budgets. Video-sharing sites rely on this prediction to promote credible videos and curb violative videos. Although deep learning champions viewership prediction, it lacks interpretability, which is fundamental to increasing the adoption of predictive models and prescribing measurements to improve viewership. Following the design-science paradigm, we propose a novel interpretable IT system, Precise Wide and Deep Learning (PrecWD), to precisely interpret viewership prediction. Improving upon state-of-the-art frameworks, PrecWD offers precise feature effects and designs an unstructured component. PrecWD outperforms benchmarks in two contexts: health video viewership prediction and misinformation viewership prediction. A user study confirms the superior interpretability of PrecWD. This study contributes to IS design theory with generalizable design principles and an interpretable predictive framework. Our findings provide implications to improve video viewership and credibility.