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dc.contributor.authorMills, Granten
dc.contributor.authorDeka, Lipikaen
dc.contributor.authorPrice, Andrewen
dc.contributor.authorRich-Mahadkar, Sameedhaen
dc.contributor.authorPantzartzis, Efthimiaen
dc.contributor.authorSellars, Peteren
dc.date.accessioned2017-10-18T10:27:35Z
dc.date.available2017-10-18T10:27:35Z
dc.date.issued2015-06-19
dc.identifier.citationMills, G. Deka, L. Price, A. Rich-Madhakar, S. Pantzartiz, E. and Sellers, P. (2015) Critical infrastructure risk in NHS England: predicting the impact of building portfolio age. International Journal of Strategic Property Management,19(2), pp.159-172en
dc.identifier.issn1648715X
dc.identifier.urihttp://hdl.handle.net/2086/14645
dc.descriptionOpen access article
dc.description.abstractNHS Trusts in England must adopt appropriate levels of continued investment in routine and backlog maintenance if they are to ensure critical backlog does not accumulate. This paper presents the current state of critical backlog maintenance within the National Health Service (NHS) in England through the statistical analyses of 115 Acute NHS Trusts. It aims to find empirical support for a causal relationship between building portfolio age and year-on-year increases in critical backlog. It makes recommendations for the use of building portfolio age in strategic asset management. The current trend across this sample of NHS Trusts may be typical of the whole NHS built asset portfolio and suggests that most Trusts need to invest between 0.5 and 1.5 per cent of income (depending upon current critical backlog levels and Trust age profile) to simply maintain critical backlog levels. More robust analytics for building age, condition and risk-adjusted backlog maintenance are required.en
dc.language.isoen_USen
dc.publisherTaylor & Francisen
dc.subjectBack Logen
dc.subjectNHSen
dc.subjectInfrastructureen
dc.subjectData Analysisen
dc.titleCritical infrastructure risk in NHS England: predicting the impact of building portfolio ageen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.3846/1648715X.2015.1029562
dc.researchgroupDIGITSen
dc.peerreviewedYesen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceN/Aen
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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