Research Snapshots

Modeling patients' illness perception and equilibrium analysis of their doctor shopping behaviour

Professor Pengfei Guo, from the Department of Management Sciences, along with co-authors, has studied the behaviour known as "doctor shopping," which occurs when a patient disagrees with a doctor's diagnosis and seeks multiple opinions without referrals. The study aims to understand why patients engage in this behaviour and determine the best decisions they can make when doctor shopping. The research shows that after each visit to a doctor, patients adjust their beliefs about their health based on new information. The decision to continue doctor shopping is influenced by factors such as how accurate the diagnosis is, the relative value of identifying a severely ill patient, and the cost of each visit. The study also examines the impact of doctor shopping on the overall well-being of society. This is assessed from two perspectives: whether doctor shopping improves the accuracy of diagnosing a patient's health condition and whether it helps alleviate patients' anxiety. The findings suggest that allowing patients to doctor shop can lead to increased congestion in the healthcare system. However, it can also benefit patients who choose to engage in this behaviour by providing them with better outcomes. There is a certain threshold of accuracy for diagnoses, above which doctor shopping reduces overall welfare and below which it improves welfare. This threshold increases as patients become more pessimistic or have diverse initial perceptions of their illness. The objective welfare maximization prefers a higher doctor shopping rate than the subjective welfare maximization only when the value of identifying a severely ill patient is high enough.

This finding helps explain why doctor shopping is often encouraged for critical illnesses like cancer.

Huang, Fengfeng; Guo, Pengfei; Wang, Yulan. "Modeling patients' illness perception and equilibrium analysis of their doctor shopping behaviour." March 2022; In: Production and Operations Management. Vol. 31, No. 3, pp. 1216-1234

Reference:
https://onlinelibrary.wiley.com/doi/full/10.1111/poms.13606