Impact of Physician Payment Scheme on Diagnostic Effort and Testing

Elodie Adida () and Tinglong Dai ()
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Elodie Adida: School of Business, University of California, Riverside, Riverside, California 92521
Tinglong Dai: Carey Business School, Johns Hopkins University, Baltimore, Maryland 21202; Hopkins Business of Health Initiative, Johns Hopkins University, Washington, DC 20001

Management Science, 2024, vol. 70, issue 8, 5408-5425

Abstract: Diagnostic errors are common and can result in serious patient harm. Making the right diagnosis often requires significant diagnostic effort. Yet most physician payment schemes are procedure based and do not account for diagnostic effort or accuracy because of observability issues. In this paper, we develop a parsimonious model to examine the impact of a physician payment scheme on a physician’s decisions to (1) exert diagnostic effort and (2) perform a confirmatory test. High effort provides an informative (though imperfect) signal of the patient’s true state; the test is confirmatory in that it is a prerequisite for diagnosing a severe condition. Our model uses a two-step diagnostic process to capture the interaction between the physician’s diagnostic effort and testing decisions. We show that under a fee-for-service payment scheme, the physician may view the diagnostic effort and the confirmatory test as either complementary or substitutive, depending on the additional revenue from testing. We also reveal nonmonotonic properties such that a more patient-centered physician may not exert more effort or provide a more accurate diagnosis. In addition, either a flat or differentiated payment scheme may be optimal. We also show that an alternative payment scheme, under which the revenue from the confirmatory test is contingent on its result, can induce the social optimum under certain conditions. With the advent of artificial intelligence as part of the standard of care and its increasing use as confirmatory tests, our research has implications for the design of physician payment systems in light of concerns about the potential erosion of individual attention.

Keywords: diagnostic effort; diagnostic testing; physician payment; healthcare operations management; artificial intelligence (search for similar items in EconPapers)
Date: 2024
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