Conspicuous by Its Absence: Diagnostic Expert Testing under Uncertainty
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We study the problem a diagnostic expert (e.g., a physician) faces when offering a diagnosis to a client (e.g., a patient) that may be based only on her own diagnostic ability or supplemented by a diagnostic test—conventional and artificial intelligence (AI) tools alike—revealing the client’s true condition. The expert’s diagnostic ability (or type) is her private information. The expert is impurely altruistic in that she cares about both the client’s utility and her own reputational payoff that depends on the peer perception about her diagnostic ability. The decision of whether to perform the test, which is costly for the client, provides the expert with an opportunity to influence that perception. We show a unique separating equilibrium exists in which the high-type expert does not resort to diagnostic testing and offers a diagnosis based only on her own diagnostic ability, whereas the low-type expert performs the test. Furthermore, we establish that the high-type expert may skip necessary diagnostic tests to separate her from the low-type expert. Interestingly, the effect of reputational payoff on under-testing is non-monotonic, and the desire to appear of high type leads to under-testing only when the reputational payoff is intermediate. Our results also suggest a more altruistic expert may be more likely to engage in under-testing. Furthermore, efforts to encourage testing by providing financial incentives or by raising malpractice-lawsuit concerns may, surprisingly, help fuel under-testing in the equilibrium. Our paper sheds new light on barriers to the adoption of AI tools aimed at enhancing physicians’ diagnostic decision making.