Modeling Patients' Illness Perception and Equilibrium Analysis of Their Doctor Shopping Behavior

When a patient's illness perception is inconsistent with a doctor's diagnosis result, she may seek for more doctoral opinions, a behavior called doctor shopping. Patients then update their beliefs about their health status, following the Bayes' rule. In this study, we model and derive patients' optimal doctor shopping decisions by adopting a `one-step look-ahead' rule. We show that the expected number of times that a patient conducts doctor-shopping is critically affected by the diagnosis accuracy, the relative value of identifying an ill patient and the price per visit. We then examine the impact of patients' doctor shopping behavior on the social welfare from two aspects, namely, an objective one that concerns about whether doctor shopping helps improve the judgment accuracy regarding the patient's health status, and a subjective one that assesses whether doctor shopping helps to relieve patient's anxiety. We find that allowing doctor shopping almost always improves the subjective social welfare as along as the false-negative and false-positive error rate difference is not large. Doctor shopping also improves the objective social welfare when at least one of the following three conditions is satisfied: the diagnostic accuracy is not extremely high, the difference between the two error rates is small, and the relative value of identifying an ill patient is large. Moreover, the welfare improvement brought by doctor shopping increases with the relative value of identifying an ill patient.