Supply-demand matching in a complex telemedicine environment considering intermediary intervention

Abstract

Matching patients and providers of medical care is necessary to ensure the most efficient use of resources and improve patient satisfaction in telemedicine. Different from the conventional two-sided matching model, this study involves three parties in telemedicine, the patients, primary care physicians (PCPs) acting as both intermediaries and demanders of the match, and the urban specialists. This paper proposes a novel method for matching this complex telemedicine supply-demand, incorporating the dual roles of PCPs as intermediaries and demanders. In order to model the complex psychological perception of matching subjects, cloud-model-based matching is integrated with prospect theory, which optimizes cloud-model-based preference utility calculations as well as disappointment theory for calculating the disappointment value and elation value of matching subjects to obtain the modified preference utility function. In addition, the peer effect and grey relation analysis are used to measure the influence of PCPs on patients, and a multi-objective optimization function is developed to maximize the preference utility values of patients, PCPs, and specialists, and minimize the difference between supply and demand subjects. The proposed matching method achieves optimal matches and overall satisfaction of subjects, along with exploring how matching contributes to a complex telemedicine environment.