International Conference on Economics, Finance & Business, London

INTER-ORGANISATIONAL NETWORK STRUCTURE IN THE HEALTHCARE ECOSYSTEM

FUMIHIKO ISADA

Abstract:

The purpose of this study is to conduct an empirical investigation of healthcare ecosystems around the world and identify the characteristics of central organisations within inter-organisational networks. Against the backdrop of advances in digital network technologies, the importance of healthcare ecosystems—in which various organisations such as healthcare organisations and IT vendors collaborate to integrate, optimise, and streamline various healthcare-related activities, from disease prevention to treatment, recovery, and health promotion—has been growing worldwide. The research question of this study is to identify which organisations are promoting the growth of healthcare ecosystems. As a research method, information on healthcare ecosystems around the world was collected from newspaper articles and press releases, and social network analysis techniques were used to analyse the relationships between organisations. The results of the analysis revealed the diversity of organisations promoting the growth of healthcare ecosystems. In other words, in addition to traditional healthcare-related organisations, various organisations such as IT and AI vendors, government agencies, universities, and financial institutions were at the centre of inter-organisational collaboration. Furthermore, the inter-organisational network structure of these central organisations was relatively open and diverse, serving as a bridge between different types of organisations. In traditional regional healthcare collaboration, it is assumed that a relatively closed and close-knit collaboration structure between a limited number of organisations centred on large hospitals was common. On the other hand, in a growing healthcare ecosystem, it is expected that the inter-organizational collaboration structure will be more open and diverse.

Keywords: Healthcare ecosystem, Inter-organisational networks, Social network analysis