Social Network Analysis in Healthcare Systems

Social network analysis (SNA) offers a suite of quantitative and visual tools to map, characterise and interpret relationships among actors in healthcare systems. By representing individuals, teams or institutions as nodes and their interactions—such as communication, advice-seeking or patient sharing—as edges, SNA illuminates patterns of collaboration, influence and resource flow. Centrality metrics identify key opinion leaders or information brokers, while community detection algorithms reveal clusters of tightly connected actors. Density measures signal the overall cohesion of a care network, and analyses of structural holes expose gaps where communication may falter. Applications span clinical coordination, diffusion of innovations, organisational redesign and public health surveillance. In hospital settings, SNA has been used to optimise emergency-department teamwork and trace patient referrals; in health systems research, it underpins interventions to enhance multidisciplinary collaboration and streamline secure messaging among professionals. The global significance of SNA in healthcare arises from its capacity to guide targeted interventions, improve patient outcomes, and inform policy decisions on resource allocation and integrated care pathways.

Research from Nature Portfolio

A foundational study constructed patient-centric care networks across multiple hospitals and derived physician collaboration networks based on shared patients. A multi-level regression framework assessed how attributes at the physician level—such as average visit load and diversity of collaborators—and at the community level, including network density and modularity, influenced two key outcomes: hospitalisation cost and length of stay. Higher average visits per physician predicted increases in both cost and length of stay, while a larger number of distinct collaborating physicians was associated primarily with rising costs, moderated by patient age, gender and comorbidity. Crucially, inter-hospital variation in community structure and density signified that network topology modulates the impact of individual physician behaviour on system-level outcomes.

Social Network Analysis in Healthcare Systems publication trend

The graph below shows the total number of articles in social network analysis in healthcare systems across all publications each year (not limited to Nature Index journals).

Technical terms

Node: An actor in the network, such as an individual clinician, department or institution.

Edge: A link between nodes representing a relationship or interaction (e.g. advice-seeking, patient sharing, messaging).

Centrality: Quantitative measures of a node’s influence or connectivity within a network (for example, degree or betweenness centrality).

Community detection: Computational methods for partitioning a network into groups of nodes that are more densely connected internally than with the rest of the network.

Density: The ratio of actual edges to all possible edges in a network, indicating overall cohesion.

References

1. Social Network Analysis in Healthcare Settings: A Systematic Scoping Review. PLOS ONE (2012).
2. Between-group behaviour in health care: gaps, edges, boundaries, disconnections, weak ties, spaces and holes. A systematic review. BMC Health Services Research (2010).
3. Network analysis of team communication in a busy emergency department. BMC Health Services Research (2013).
4. Exploring the impact of different multi-level measures of physician communities in patient-centric care networks on healthcare outcomes: A multi-level regression approach. Scientific Reports (2016).
5. Networked Behaviors Associated With a Large-Scale Secure Messaging Network: Cross-Sectional Secondary Data Analysis. JMIR Medical Informatics (2025).
6. Network Analysis of Academic Medical Center Websites in the United States. Scientific Data (2023).
7. Use of social network analysis methods to study professional advice and performance among healthcare providers: a systematic review. Systematic Reviews (2017).