Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance
Publication Date
October 02, 2017
Authors
Daniel M. Bean, Clive Stringer, Neeraj Beeknoo, James Teo, et al
Volume
12
Issue
10
Pages
e0185912
DOI
https://dx.plos.org/10.1371/journal.pone.0185912
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0185912
Scopus
85030256219
Mendeley
http://www.mendeley.com/research/network-analysis-patient-flow-two-uk-acute-care-hospitals-identifies-key-subnetworks-ae-performance
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Mendeley | Further Information

{"title"=>"Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance", "type"=>"journal", "authors"=>[{"first_name"=>"Daniel M.", "last_name"=>"Bean", "scopus_author_id"=>"57195940902"}, {"first_name"=>"Clive", "last_name"=>"Stringer", "scopus_author_id"=>"57195934717"}, {"first_name"=>"Neeraj", "last_name"=>"Beeknoo", "scopus_author_id"=>"57192207134"}, {"first_name"=>"James", "last_name"=>"Teo", "scopus_author_id"=>"57195934746"}, {"first_name"=>"Richard J.B.", "last_name"=>"Dobson", "scopus_author_id"=>"8931612400"}], "year"=>2017, "source"=>"PLoS ONE", "identifiers"=>{"doi"=>"10.1371/journal.pone.0185912", "sgr"=>"85030256219", "isbn"=>"1111111111", "scopus"=>"2-s2.0-85030256219", "issn"=>"19326203", "pui"=>"618513269"}, "id"=>"42a2b158-6845-3546-af71-2d5c36ce4758", "abstract"=>"The topology of the patient flow network in a hospital is complex, comprising hundreds of overlapping patient journeys, and is a determinant of operational efficiency. To understand the network architecture of patient flow, we performed a data-driven network analysis of patient flow through two acute hospital sites of King's College Hospital NHS Foundation Trust. Administration databases were queried for all intra-hospital patient transfers in an 18-month period and modelled as a dynamic weighted directed graph. A 'core' subnetwork containing only 13-17% of all edges channelled 83-90% of the patient flow, while an 'ephemeral' network constituted the remainder. Unsupervised cluster analysis and differential network analysis identified sub-networks where traffic is most associated with A&E performance. Increased flow to clinical decision units was associated with the best A&E performance in both sites. The component analysis also detected a weekend effect on patient transfers which was not associated with performance. We have performed the first data-driven hypothesis-free analysis of patient flow which can enhance understanding of whole healthcare systems. Such analysis can drive transformation in healthcare as it has in industries such as manufacturing.", "link"=>"http://www.mendeley.com/research/network-analysis-patient-flow-two-uk-acute-care-hospitals-identifies-key-subnetworks-ae-performance", "reader_count"=>3, "reader_count_by_academic_status"=>{"Student > Bachelor"=>1, "Unspecified"=>1, "Student > Master"=>1}, "reader_count_by_user_role"=>{"Student > Bachelor"=>1, "Unspecified"=>1, "Student > Master"=>1}, "reader_count_by_subject_area"=>{"Medicine and Dentistry"=>1, "Unspecified"=>1, "Agricultural and Biological Sciences"=>1}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Unspecified"=>{"Unspecified"=>1}}, "group_count"=>0}

Scopus | Further Information

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