Social network analyses of patient-healthcare worker interactions: implications for disease transmission.

Patients and healthcare workers (HCW) in healthcare settings represent a unique social network in which the risk of transmission of an infection is considered to be higher for both HCW and patients. Using data from existing clinical informatics resources, we constructed social networks of patient-HCW interactions in the emergency department of a tertiary care pediatric hospital. The structural properties of these networks were analyzed and compared to other well known networks. Patient-HCW networks do not demonstrate the classical power-law distribution of scale-free networks, thus indicating that they are different from social networks of individuals in a community. The clustering coefficient is larger as compared to a random network, indicating small world properties. The eigenvector centrality, used to identify the most important nodes, reveals HCW to be more connected than patients. These properties imply differences that must be taken into account when analyzing patient-HCW networks and planning interventions and mitigation strategies to prevent the spread of infectious diseases in healthcare settings.