A Dual Referral Optimization Model for Medical Clusters Based on Queuing Theory and Cooperative Game Incentives

In this paper, An optimization model for dual referral of medical treatment combination is established via queuing theory and cooperative game incentive mechanism. We establishes a queuing theory model for dual referral in the medical association and analyzes the impact of the referral rate on the revenue and crowding degree of central and community hospitals under the medical association model. In the form of medical alliance, when the referral rate continues to increase, the division of labor between central hospitals and community hospitals becomes more and more clear. Central hospitals focus on high-technology medical treatment such as surgery, while community hospitals focus on basic medical treatment such as rehabilitation, which can maximize the overall benefits. To reduce the overall congestion of the system, the higher the referral rate is not the better, it needs the joint coordination of central and community hospitals. In the initial decision making, the central hospital in the medical consortium can choose a higher referral rate, and at the same time, it should consider the resource limitation of the community hospital and cooperate with the community hospital with more beds. Secondly, through the cooperative game theory, different incentive measures are designed to analyze the situation of simultaneously rewarding central hospitals and community hospitals and only rewarding central hospitals. The latter can achieve a higher referral rate faster with less funds, and the optimal investment capital is about half of the original revenue. Meanwhile, the willingness of community hospitals to cooperate should be considered to achieve a win-win result.