A Theory of Information Biases on Healthcare Platforms

  • Zhenhua Wu
  • Zhijie LinEmail author
  • Yong Tan
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 328)


In this paper, we present a theory of information biases generated by online physicians/doctors who work on healthcare platforms. We find that the private information that originates from the expertise of the physicians and their professional investigations on patients’ reports/messages would induce a persistent bias on diagnostic reports. This information bias would further influence the demand of online healthcare services in two ways. First, the more skeptical the rational patients are towards the potentially biased diagnostic information, the less likely their decision would rely on the diagnostic reports generated by the physicians. Second, the information bias would make certain types of diagnosis (medical reports) come up more often than others. We also find that the private information gives online physicians more incentive to bias their reports if their return of career concern depends on the reputation of being providers of accurate diagnostic reports. For the healthcare platform, we find that the bias can be reduced by restricting the discretion allowed to physicians, but the platform’s profit would be increased if more bias is allowed. We also present a variety of testable predictions related to the registration fee charged by healthcare platforms.


Healthcare platform Information bias Information provider Online physicians Asymmetric information Bayesian update Perfect bayesian equilibrium 


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© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of ManagementNanjing UniversityNanjingChina
  2. 2.Foster School of BusinessUniversity of WashingtonSeattleUSA

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