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PREDICTING CONSULTATION SUCCESS ON ONLINE HEALTH PLATFORMS USING DYNAMIC KNOWLEDGE NETWORKS AND MULTIMODAL DATA FUSION.

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Title: PREDICTING CONSULTATION SUCCESS ON ONLINE HEALTH PLATFORMS USING DYNAMIC KNOWLEDGE NETWORKS AND MULTIMODAL DATA FUSION.
Authors: Geng, Shuang (AUTHOR) gs@szu.edu.cn, Zhang, Wenli (AUTHOR) wlzhang@iastate.edu, Xie, Jiaheng (AUTHOR) jxie@udel.edu, Liang, Gemin (AUTHOR) gemin@bit.edu.cn, Niu, Ben (AUTHOR) drniuben@gmail.com, Ram, Sudha (AUTHOR) sram@arizona.edu
Source: MIS Quarterly. Sep2026, Vol. 50 Issue 3, p909-940. 32p. 2 Color Photographs, 3 Diagrams, 12 Charts, 2 Graphs.
Subject Terms: *Digital platforms, *Forecasting, *Artificial neural networks, Digital health, Data fusion (Statistics), Medical consultation, Patients
Abstract: 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. [ABSTRACT FROM AUTHOR]
Copyright of MIS Quarterly is the property of MIS Quarterly and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: PREDICTING CONSULTATION SUCCESS ON ONLINE HEALTH PLATFORMS USING DYNAMIC KNOWLEDGE NETWORKS AND MULTIMODAL DATA FUSION.
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  Data: <searchLink fieldCode="AR" term="%22Geng%2C+Shuang%22">Geng, Shuang</searchLink> (AUTHOR)<i> gs@szu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Zhang%2C+Wenli%22">Zhang, Wenli</searchLink> (AUTHOR)<i> wlzhang@iastate.edu</i><br /><searchLink fieldCode="AR" term="%22Xie%2C+Jiaheng%22">Xie, Jiaheng</searchLink> (AUTHOR)<i> jxie@udel.edu</i><br /><searchLink fieldCode="AR" term="%22Liang%2C+Gemin%22">Liang, Gemin</searchLink> (AUTHOR)<i> gemin@bit.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Niu%2C+Ben%22">Niu, Ben</searchLink> (AUTHOR)<i> drniuben@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Ram%2C+Sudha%22">Ram, Sudha</searchLink> (AUTHOR)<i> sram@arizona.edu</i>
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  Data: <searchLink fieldCode="JN" term="%22MIS+Quarterly%22">MIS Quarterly</searchLink>. Sep2026, Vol. 50 Issue 3, p909-940. 32p. 2 Color Photographs, 3 Diagrams, 12 Charts, 2 Graphs.
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  Data: *<searchLink fieldCode="DE" term="%22Digital+platforms%22">Digital platforms</searchLink><br />*<searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+neural+networks%22">Artificial neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+health%22">Digital health</searchLink><br /><searchLink fieldCode="DE" term="%22Data+fusion+%28Statistics%29%22">Data fusion (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+consultation%22">Medical consultation</searchLink><br /><searchLink fieldCode="DE" term="%22Patients%22">Patients</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of MIS Quarterly is the property of MIS Quarterly and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.25300/MISQ/2025/19444
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      – Code: eng
        Text: English
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        PageCount: 32
        StartPage: 909
    Subjects:
      – SubjectFull: Digital platforms
        Type: general
      – SubjectFull: Forecasting
        Type: general
      – SubjectFull: Artificial neural networks
        Type: general
      – SubjectFull: Digital health
        Type: general
      – SubjectFull: Data fusion (Statistics)
        Type: general
      – SubjectFull: Medical consultation
        Type: general
      – SubjectFull: Patients
        Type: general
    Titles:
      – TitleFull: PREDICTING CONSULTATION SUCCESS ON ONLINE HEALTH PLATFORMS USING DYNAMIC KNOWLEDGE NETWORKS AND MULTIMODAL DATA FUSION.
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            NameFull: Geng, Shuang
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            – D: 01
              M: 09
              Text: Sep2026
              Type: published
              Y: 2026
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