JOURNAL ARTICLE

On-Demand Healthcare Platforms: Impact of Question and Answer Service on Online Consultations and Offline Appointments.

  • Published In: Information Systems Research (INFORMS), 2026, v. 37, n. 1. P. 454 1 of 3

  • Database: Business Source Ultimate 2 of 3

  • Authored By: Liu, Yixuan; Agarwal, Ashish; Lai, Guoming; Zhou, Weihua 3 of 3

Abstract

This article examines the impact of low-cost question and answer (Q&A) services integrated into on-demand healthcare platforms, using detailed data from a major Chinese platform. The study finds that Q&A services increase demand for both online consultations (by 2%) and offline appointments (by 4.3%), as well as boost consultation revenue by 6.6%. The Q&A service facilitates better patient-provider matching, encourages follow-ups with the same or other doctors—including specialists and those with higher professional titles—and reduces future revisits and browsing for medical information, suggesting improved healthcare outcomes. The research identifies three key mechanisms behind these effects: a sampling effect (reducing uncertainty about providers), a spillover effect (raising awareness of additional care needs), and a matching effect (guiding patients to appropriate specialties). The findings have implications for platform design, provider incentives, and healthcare resource management, highlighting the role of scalable, information-based tools in improving care access and coordination in fragmented healthcare systems.

Additional Information

  • Source:Information Systems Research (INFORMS). 2026/03, Vol. 37, Issue 1, p454
  • Document Type:Article
  • Subject Area:Computer Science
  • Publication Date:2026
  • ISSN:1047-7047
  • DOI:10.1287/isre.2023.0644
  • Accession Number:192724219
  • Copyright Statement:Copyright of Information Systems Research (INFORMS) is the property of INFORMS: Institute for Operations Research & the Management Sciences 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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