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Can increased integration of spatial analysis improve the understanding of how hospital design may affect healthcare associated infection dynamics and patient safety?

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Title: Can increased integration of spatial analysis improve the understanding of how hospital design may affect healthcare associated infection dynamics and patient safety?
Authors: Davis, Grahame
Item Type: Thesis or dissertation
Abstract: Background: Healthcare associated infections (HCAIs) are considered to be the most frequent adverse event that threatens patients’ safety worldwide. A great deal of work has been carried out looking to improve surveillance, control and prevention of HCAIs within the NHS and health systems worldwide. Like in other research settings, an understanding of the localised environment is critical to understand its potential effect on disease dynamics. Numerous techniques, not yet exploited within healthcare settings, have been developed to quantify this environmental impact. Aim: To examine how integration of spatial epidemiology and modelling could improve the understanding of how hospital design may affect healthcare associated infection dynamics and patient safety, allowing improvement of HCAI investigations within the NHS. Methods: This thesis made use of data from Imperial College Healthcare NHS Trust, including hospital floorplans, information on patient transfers, laboratory tests and traditional infection surveillance data. A literature review was carried out to identify limitations in the application of spatial epidemiology within healthcare settings. Geographical Information Systems (GIS), graph-mining and network modelling were applied to analyse existing datasets, leading to the production of ward maps and networks. Results: The key findings of the work were numerous. Digitalisation of ward maps allows for wider dissemination of this data to infection control teams and other researchers to aid in investigations. Certain ward characteristics appear associated with increased numbers of infections and the physical layout of the ward showed relations between ward subgraphs and infection risk in both positive and negative manners. Network modelling allowed visualisation and analysis of patient movements within and between hospitals. Allowed identification of how wards are linked and specifically which wards merge departments/specialities and link patient populations. Identifiable control points allowed for greater understanding of the ward network and this was shown to help with response and planning for extreme or emergency situations. Conclusion: These techniques, whilst commonly used in other areas, have been underutilised within healthcare and have within this thesis been shown to extract greater information from pre-existing data. The work identified numerous ways existing datasets can be further explored to help researchers; whilst also providing outputs that can help day to day work, such as ward maps.
Content Version: Open Access
Issue Date: Apr-2017
Date Awarded: Nov-2018
URI: http://hdl.handle.net/10044/1/78848
DOI: https://doi.org/10.25560/78848
Copyright Statement: Creative Commons Attribution Non-Commercial No Derivatives licence
Supervisor: Drumright, Lydia
Sevdalis, Nick
Sponsor/Funder: UK Clinical Research Collaboration
Department: Department of Medicine
Publisher: Imperial College London
Qualification Level: Doctoral
Qualification Name: Doctor of Philosophy (PhD)
Appears in Collections:Medicine PhD theses