Bipartite Supplier-heathcare systems Network Analysis for Detecting Communities and Strategic Dependencies in Chile’s Public Procurement of Medical Equipment

Abstract

Open government data have significantly increased the availability of information about procurement processes. However, despite the growing use of network analysis to study economic and institutional interactions, structural dependency between suppliers and health institutions has rarely been operationalized as a measurable property of procurement networks. This limitation is particularly relevant in medical equipment markets, where supplier concentration may affect the resilience and continuity of healthcare services. This study models Chile’s medical equipment procurement system as a weighted bipartite network constructed from awarded purchase orders published on the national e-procurement platform (www.mercadopublico.cl) between January 2022 and April 2025. The resulting network connects 204 healthcare institutions with 249 suppliers through 1,420 procurement transactions. Community detection based on modularity optimization reveals eleven structural groups characterized by dense internal connectivity and sparse inter-group links. The results indicate a structural organization of the market. High-value procurement relationships are concentrated in specific technological segments, particularly imaging and surgical equipment, where a small number of multinational suppliers dominate much of the transaction volume. In contrast, other communities display diversified procurement patterns involving multiple suppliers and mid-range purchase values. These findings provide a structural characterization of Chile’s medical equipment procurement ecosystem and suggest that network-based approaches can support the identification of supplier dependency risks in healthcare markets. By translating large volumes of procurement data into interpretable network structures, the framework contributes to the development of tools that can support evidence-based procurement planning and risk monitoring in health technology management.