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Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach

Author

Listed:
  • Tongxin Zhou

    (W. P. Carey School of Business, Arizona State University, Tempe, Arizona 85287)

  • Yingfei Wang

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

  • Lu (Lucy) Yan

    (Kelley School of Business, Indiana University, Bloomington, Indiana 47405)

  • Yong Tan

    (Michael G. Foster School of Business, University of Washington, Seattle, Washington 98195)

Abstract

Online healthcare platforms provide users with various intervention programs to promote personal wellness. Given the many options available, it’s often difficult for individuals to decide in which intervention to participate, especially when they lack the experience or knowledge to evaluate the interventions. This may discourage individuals’ continuous engagement in online health management. In this study, we are motivated to develop a personalized healthcare recommendation framework to help individuals better discover the interventions that fit their needs. Considering the challenges in intervention adaptation and diversification in a highly dynamic online healthcare environment, we propose an innovative online learning framework that synthesizes deep representation learning and a theory-guided diversity promotion scheme. We evaluate our approach through a real-world data set on users’ intervention participation in an online weight-loss community. Our results provide strong evidence for the effectiveness of our proposed recommendation framework and each of its design components. Our study contributes to the emerging information systems research on prescriptive analytics and the application of business intelligence. The proposed modeling framework and evaluation results offer important implications for multiple stakeholders, including online healthcare platforms, policymakers, and users.

Suggested Citation

  • Tongxin Zhou & Yingfei Wang & Lu (Lucy) Yan & Yong Tan, 2023. "Spoiled for Choice? Personalized Recommendation for Healthcare Decisions: A Multiarmed Bandit Approach," Information Systems Research, INFORMS, vol. 34(4), pages 1493-1512, December.
    • Handle: RePEc:inm:orisre:v:34:y:2023:i:4:p:1493-1512
    DOI: 10.1287/isre.2022.1191
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    References listed on IDEAS

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    1. Volodymyr Mnih & Koray Kavukcuoglu & David Silver & Andrei A. Rusu & Joel Veness & Marc G. Bellemare & Alex Graves & Martin Riedmiller & Andreas K. Fidjeland & Georg Ostrovski & Stig Petersen & Charle, 2015. "Human-level control through deep reinforcement learning," Nature, Nature, vol. 518(7540), pages 529-533, February.
    2. Lu Yan & Yong Tan, 2014. "Feeling Blue? Go Online: An Empirical Study of Social Support Among Patients," Information Systems Research, INFORMS, vol. 25(4), pages 690-709, December.
    3. Bandura, Albert, 1991. "Social cognitive theory of self-regulation," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 248-287, December.
    4. Lu (Lucy) Yan, 2018. "Good Intentions, Bad Outcomes: The Effects of Mismatches between Social Support and Health Outcomes in an Online Weight Loss Community," Production and Operations Management, Production and Operations Management Society, vol. 27(1), pages 9-27, January.
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    Cited by:

    1. Hu, Xiao & Kang, Siqin & Ren, Long & Zhu, Shaokeng, 2024. "Interactive preference analysis: A reinforcement learning framework," European Journal of Operational Research, Elsevier, vol. 319(3), pages 983-998.
    2. Xue, Jiaxin & Deng, Zhaohua & Wang, Bin, 2025. "Effects of emotional expression and platform prompts on patient selection: A multi-method study on online question and answer platforms," Social Science & Medicine, Elsevier, vol. 370(C).

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