Impact of collaboration network on care costs: an integrated healthcare analysis

While prior research on inpatient care costs has primarily focused on patient- and clinical-level factors, limited empirical attention has been given to how physician collaboration shapes cost outcomes. Few studies have examined this relationship using social network analysis at the micro level. This study investigates how collaboration networks influence care costs, the mechanisms through which these effects occur, and the moderating role of attending physicians' workload. The structure of collaboration networks determines how efficiently information is shared and decisions are made, which in turn influences healthcare costs. Physicians' centrality within the network impacts their ability to access information and facilitate knowledge transfer, with higher centrality promoting better collaboration, reducing redundancies, and improving decision-making. Using digital trace data from a hospital in China, we employed social network analysis to identify collaborative networks and fitted a log-linear model to examine the association between these networks and healthcare costs. The results demonstrate that degree and closeness centrality of the attending physicians are negatively correlated with hospitalization cost. In contrast, betweenness centrality was found positively correlated with hospitalization cost. Additionally, we find that centrality metrics help reduce diagnostic and treatment costs by enhancing information exchange and clinical decision-making. Furthermore, the workload of attending physicians significantly impacted the relationship between collaboration network centrality and care costs. Specifically, the combined effect of an attending physician's degree and workload has an additional negative impact on hospitalization costs. The interaction between betweenness centrality and workload was found to be positively correlated with hospitalization costs. As the healthcare industry continues to evolve towards more collaborative and integrated models, these findings contribute to guiding effective and cost-efficient healthcare delivery.