Two-way referral cooperative hospital selection with uncertain information: A two-sided matching decision-making approach

Abstract

To better rationalize the allocation of medical resources and improve the efficiency of medical resource utilization, researchers have focused on the problem of two-way referral cooperation among hospitals. The selection of an appropriate referral hospital constitutes a crucial aspect of a two-way referral system, which affects the quality of patient treatment and the financial outcome of hospital operations. Within this context, this study regards the collaborative referral hospital selection problem as a two-sided matching decision-making process. Given the hesitance and bounded rationality of decision-makers, a novel two-sided matching framework is developed that integrates hesitant fuzzy linguistic term sets and regret theory to more accurately reflect real-world conditions. Specifically, evaluation information is converted to hesitant fuzzy linguistic term sets via context-free grammar, and new utility functions are introduced to calculate the utility value, regret utility, and rejoice utility through the hesitant degree and score function. Furthermore, an optimization model for calculating the criteria weight is established based on the Euclidean distance and maximization deviation method. Ultimately, the matching satisfaction degree is determined, and a biobjective programming model that maximizes the overall matching satisfaction degree is formulated and solved. The results from experiments and analysis indicate that the proposed framework produces optimal and stable matching solutions, thereby providing a useful reference for hospitals seeking satisfactory referral partners. Moreover, this model could be extended to address large-scale two-sided matching problems or to refine the referral hospital selection problem by considering individual patient needs.