The popularity of social networks is growing and offering promising opportunities for practical applications, such as computer networks, social systems, physical systems, and biological networks. The use of networks provides valuable insights into the patterns, relationships, and behavior of various entities and their interactions. Healthcare service systems are critical social systems that can be analyzed using network-based methodologies, one of which is influence maximization (IM). It is a network analysis technique that identifies a small subset of suppliers in a network that has the most influence on the overall behavior of the healthcare system. By utilizing network analysis techniques, data scientists can identify the most crucial healthcare providers in a network and better understand the system's operation. The paper uses a network global structure-based centrality (ngsc) approach that combines conventional k-shell and the sum of neighbors' degree methods with information about the network's global structural features to identify significant providers in the provider-interacting network. The experimental outcomes on five real-world networks reveal that the proposed NGSC technique outperforms earlier benchmark methods in identifying significant providers under a spreading dynamic when using the IC model. [ABSTRACT FROM AUTHOR]