Influential factors of intercity patient mobility and its network structure in China

Intercity patient mobility reflects the geographic mismatch between healthcare resources and the population, and has rarely been studied with big data at large spatial scales. In this paper, we investigated the patterns of intercity patient mobility and factors influencing this behavior based on >4 million hospitalization records of patients with chronic kidney disease in China. To provide practical policy recommendations, a role identification framework informed by complex network theory was proposed considering the strength and distribution of connections of cities in mobility networks. Such a mobility network features multiscale community structure with “universal administrative constraints and a few boundary breaches”. We discovered that cross-module visits which accounted for only 20 % of total visits, accounted for >50 % of the total travel distance. The explainable machine learning modeling results revealed that distance has a power-law-like effect on flow volume, and high-quality healthcare resources are an important driving factor. This paper provides not only a methodological reference for patient mobility studies but also valuable insights into public health policies. • More than 4 million multi-year mobility records from a national database were investigated. • The distribution of travel distance of patients differs from the results of general human mobility studies. • The network features multiscale community structure with “universal administrative constraints and a few boundary breaches”. • An identification framework was proposed to understand cities’ roles in the mobility network. • The explainable AI model revealed that distance and high-quality healthcare resources are two critical factors.