Abstract
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.
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Notes
- 1.
For instance, a study (Hanauer et al. 2014) surveys 2,000 adults in the U.S. and tries to find the importance of online physician reviews.
- 2.
In reality, this belief could be formed in many different ways. For instance, patients might form this belief based on the online review on physicians or platforms.
- 3.
The existence of information asymmetry between patients and physicians has been recognized in the economics literature for a long time. For instance in Arrow (1963), he points out that: “…physician is using his knowledge to the best advantage… the patient does not, at least in his belief, know as much as the physician…”
- 4.
Previous research, e.g., Jemal et al. (2005), has noticed that chronic diseases are a major factor which causes mortality and disability in the United States. Studies (e.g., Thorpe et al. 2004) also find that chronic diseases might be the main part of the 18% of the GDP in the United States which is the total healthcare spending in the United States. According to Johns Hopkins University (2004), about 140 million Americans have at least one chronic condition, and an estimated value indicates that nearly 50% of Americans will have at least one by 2030.
- 5.
In economics, especially in the literature of contract theory, e.g., Tirole (1986), this information is also called soft information which is different from the definition of soft information in the IS literature.
- 6.
This setup induces that the number of patients is larger than the number of physicians in the market. We use this setup to capture the fact that physician is scare resource in the market.
- 7.
The goal of this paper is to analyze the bias generated by online healthcare platforms, so we assume that patients always truthfully report information about their health condition to physicians. This assumption would simplify our analysis but does not influence the main results.
- 8.
For instance, patients and physicians could communicate through online chatting functions provided by the platform (see Hao Dai Fu in the Chinese market as an example). However in this paper, we do not model the details of this communication process, but simply assume that, at the end of a communication, an honest message will always be sent from a patient to a physician.
- 9.
In reality, there might be two consultations across different platforms. In the first one, which is usually free, patients communicate with online physicians to get general information about each other. If a patient wants to have a further and more professional consultation, he needs to make a payment. The amount of payment is usually determined by the platform. In this model, we do not consider the difference between the two consultations.
- 10.
Here, we borrow the word “discretion” from the economics literature, e.g., Kydland and Prescott (1977), to describe the freedom or flexibility of online physicians’ actions. More discretion implies that online physicians face lower restriction on the contents of diagnostic reports. In a very extreme case (i.e., highest discretion), physicians could write whatever they want to write. Less discretion implies higher restriction on the contents of the diagnostic reports. For instance, the platform may put strong moral standards on physicians’ actions and does not allow them to write any information which does not consistent with the verifiable evidence.
- 11.
We could extend this to a setting with heterogeneous physicians. However, main results still hold and no new insights would be generated through this complicated extension.
- 12.
Since we assume that physicians are homogeneous in terms of their expertise, we can also use q to measure a representative physician’s expertise.
- 13.
In general, we could consider a more general case under which, with positive probability, the physicians could observe a signal indicating that the patient is in a bad condition. This generalization would only complicate our analysis, but would not affect the main results and no new insights would be generated.
- 14.
It can be proved that this is an equilibrium strategy for the physician. If we use the more general setup here, we have to deal with the issue of multiple equilibria which only complicates the analysis with no new insights.
- 15.
To be more precise, the utility defined here is the utility before paying the registration fee F.
- 16.
This assumption is purely for technical reason. If c > 1 + Ab, then no patient would act even if he/she is in a bad condition. If c < 1, each patient would act no matter what report is generated, which means there is no demand for health reports.
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Wu, Z., Lin, Z., Tan, Y. (2018). A Theory of Information Biases on Healthcare Platforms. In: Cho, W., Fan, M., Shaw, M., Yoo, B., Zhang, H. (eds) Digital Transformation: Challenges and Opportunities. WEB 2017. Lecture Notes in Business Information Processing, vol 328. Springer, Cham. https://doi.org/10.1007/978-3-319-99936-4_9
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