A dual-clustering algorithm for a robust medical grid partition problem considering patient referral

The medical grid including hospitals at all levels is a new hierarchical diagnosis and treatment system. It is assumed to provide health services for residents in a certain area, allowing free referral of patients, so as to effectively utilize medical resources. Therefore, from the perspective of the government, the key issue is how to divide the medical grid in a robust and balanced manner. In this paper, various deterministic factors, such as hospital level, location and department, as well as uncertain factors, including patient distribution or population density, are considered in decision-making. To solve this problem, a dual-clustering algorithm based on K-means and K-medoids (DCKK) is developed with local search methods to minimize the average patient waiting and travelling time. The experimental results show that DCKK algorithm can generate better and more robust grid partition solutions than the existing mainstream algorithms in different scenarios. In addition, the rules between the number of medical grids and the number of patients, as well as the hospital sharing between medical grids, are also studied. Finally, a real medical grid partition case of Ji'nan, China, with forty hospitals in four urban areas, is studied, and five medical grids are recommended.