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

Abstract

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.

Figure 1

Cumulative distribution of degree on log-log scale. Note that the distribution is not a straight line, indicating the data likely do not follow a power-law distribution.

Figure 2

The patient-HCW interaction network, scaled by eigenvector centrality. Squares presents patients; circles represent healthcare workers (HCW). The size of nodes is scaled to each node’s eigenvector centrality, and the width of ties is scaled to the length of the interaction time between nodes. Please note that the HCW are connected to both patients and other HCW. Patients do not have connections to other patients as this network represents patients in individual rooms. In color: red lines are patient-HCW connections and blue lines are HCW-HCW connections.