Premium
Network analysis of intra‐hospital transfers and hospital onset clostridium difficile infection
Author(s) -
McHaneyLindstrom Megan,
Hebert Courtney,
Miller Harvey,
MoffattBruce Susan,
Root Elisabeth
Publication year - 2020
Publication title -
health information and libraries journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.779
H-Index - 38
eISSN - 1471-1842
pISSN - 1471-1834
DOI - 10.1111/hir.12274
Subject(s) - psychological intervention , geographic information system , computer science , software , variation (astronomy) , control (management) , data mining , medicine , geography , cartography , nursing , artificial intelligence , programming language , physics , astrophysics
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 HAI s, 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.