BACKGROUND: The role of the infection preventionist (IP) has become increasingly complex, underscoring the need for rapid mechanisms of knowledge acquisition. One mechanism for knowledge acquisition is knowledge-sharing through social networks. In a state such as Kentucky with predominantly rural health care facilities, an optimal knowledge-sharing network is critical; however, descriptions of these networks are absent from the literature. The objective of this study was to evaluate the knowledge-sharing networks of hospital-based IPs in Kentucky. METHODS: A survey was sent to all hospital-based IPs in Kentucky in November 2010. Density and component analyses were used to evaluate network cohesion, and centrality statistics and key player algorithms were used to identify IPs important to the network. RESULTS: A total of 75 (58%) IPs completed the survey. The network density was 1.8%. Three components were identified. The median (range) centrality measures were as follows: in-degree, 2 (0-11); out-degree, 0.5 (0-5); betweenness, 0 (0-567); and eigenvector 0.02 (0-0.45). Three key players were identified. CONCLUSIONS: Low network statistics indicate that the knowledge-sharing network of hospital-based IPs might not be adequate for efficient knowledge-sharing. Interventions to increase the density of the network and reduce the number of components are needed.
BACKGROUND: The role of the infection preventionist (IP) has become increasingly complex, underscoring the need for rapid mechanisms of knowledge acquisition. One mechanism for knowledge acquisition is knowledge-sharing through social networks. In a state such as Kentucky with predominantly rural health care facilities, an optimal knowledge-sharing network is critical; however, descriptions of these networks are absent from the literature. The objective of this study was to evaluate the knowledge-sharing networks of hospital-based IPs in Kentucky. METHODS: A survey was sent to all hospital-based IPs in Kentucky in November 2010. Density and component analyses were used to evaluate network cohesion, and centrality statistics and key player algorithms were used to identify IPs important to the network. RESULTS: A total of 75 (58%) IPs completed the survey. The network density was 1.8%. Three components were identified. The median (range) centrality measures were as follows: in-degree, 2 (0-11); out-degree, 0.5 (0-5); betweenness, 0 (0-567); and eigenvector 0.02 (0-0.45). Three key players were identified. CONCLUSIONS: Low network statistics indicate that the knowledge-sharing network of hospital-based IPs might not be adequate for efficient knowledge-sharing. Interventions to increase the density of the network and reduce the number of components are needed.
Authors: Nikkita Gunvant Patel; Chris Rorres; Damien O Joly; John S Brownstein; Ray Boston; Michael Z Levy; Gary Smith Journal: Proc Natl Acad Sci U S A Date: 2015-06-15 Impact factor: 11.205