Impact of Physician Payment Scheme on Diagnostic Effort and Testing

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. This paper was accepted by Jayashankar Swaminathan, operations management. Funding: T. Dai was supported by the Johns Hopkins Discovery Award (2022–2024) and the Hopkins Business of Health Initiative Seed Grant (2022–2023). Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.4937 .

Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance

“…The presence of the AI option suggests that the physician may lean on AI and reduce the diagnostic effort for at least some patients. While the problem of endogenous diagnostic effort has been studied in the healthcare operations management literature (Adida & Dai, 2022), the literature has yet to examine the physician's diagnostic effort decision in light of the possibility of using AI.…”

“…To attain these 4Ps requires a systematic perspective on how AI impacts a patient's experience through each episode of care. For example, if a physician decides to use an AI tool to diagnose a patient's condition, the patient may draw from that decision to form an opinion about the physician's skill level or, in certain situations, about the amount of effort the physician puts into the diagnostic process (Adida & Dai, 2022; Dai & Singh, 2020). In anticipation of the patient's perception, the physician may opt to use (or not use) AI in ways that are not necessarily in the patient's best interests.…”