OBJECTIVES: To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment. METHODS: A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS. RESULTS: The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor. DISCUSSION: This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals. CONCLUSION: Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.
OBJECTIVES: To explore how social network analysis (SNA) can be used to analyse intra-hospital patient networks of individuals with a hospital acquired infection (HAI) for further analysis in a geographical information systems (GIS) environment. METHODS: A case and control study design was used to select 2008 patients. We retrieved locational data for the patients, which was then translated into a network with the SNA software and then GIS software. Overall metrics were calculated for the SNA based on three datasets and further analysed with a GIS. RESULTS: The SNA analysis compared cases to control indicating significant differences in the overall structure of the networks. A GIS visual representation of these metrics was developed, showing spatial variation across the example hospital floor. DISCUSSION: This study confirmed the importance that intra-hospital patient networks play in the transmission of HAIs, highlighting opportunities for interventions utilising these data. Due to spatial variation differences, further research is necessary to confirm this is not a localised phenomenon, but instead a common situation occurring within many hospitals. CONCLUSION: Utilising SNA and GIS analysis in conjunction with one another provided a data-rich environment in which the risk inherent in intra-hospital transfer networks was quantified, visualised and interpreted for potential interventions.
Authors: Brian J Douthit; Rachel L Walden; Kenrick Cato; Cynthia P Coviak; Christopher Cruz; Fabio D'Agostino; Thompson Forbes; Grace Gao; Theresa A Kapetanovic; Mikyoung A Lee; Lisiane Pruinelli; Mary A Schultz; Ann Wieben; Alvin D Jeffery Journal: Appl Clin Inform Date: 2022-02-09 Impact factor: 2.342
Authors: Emanuela Estera Boncea; Paul Expert; Kate Honeyford; Anne Kinderlerer; Colin Mitchell; Graham S Cooke; Luca Mercuri; Céire E Costelloe Journal: BMJ Qual Saf Date: 2021-01-25 Impact factor: 7.035