Interpreting social network metrics in healthcare organisations: A review and guide to validating small networks

Social network analysis is an increasingly popular sociological method used to describe and understand the social aspects of communication patterns in the health care sector. The networks studied in this area are special because they are small, and for these sizes, the metrics calculated during analysis are sensitive to the number of people in the network and the density of observed communication. Validation is of particular value in controlling for these factors and in assisting in the accurate interpretation of network findings, yet such approaches are rarely applied. Our aim in this paper was to bring together published case studies to demonstrate how a proposed validation technique provides a basis for standardised comparison of networks within and across studies. A validation is performed for three network studies comprising ten networks, where the results are compared within and across the studies in relation to a standard baseline. The results confirm that hierarchy, centralisation and clustering metrics are highly sensitive to changes in size or density. Amongst the three case studies, we found support for some conclusions and contrary evidence for others. This validation approach is a tool for identifying additional features and verifying the conclusions reached in observational studies of small networks. We provide a methodological basis from which to perform intra-study and inter-study comparisons, for the purpose of introducing greater rigour to the use of social network analysis in health care applications.

► Social network analysis of small networks is problematic when comparing networks of different sizes or densities.
► Network density and size confound aggregate network metrics, which leads to misinterpretation in published case studies.
► Comparison across networks of different size or density is possible using a simulated random-network baseline.
► The method is a way of comparing the strength of influence that social constraints have on patterns of communication.