Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance

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 acut...

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Bibliographic Details
Published in:PloS one 2017-10, Vol.12 (10), p.e0185912
Main Authors: Bean, Daniel M, Stringer, Clive, Beeknoo, Neeraj, Teo, James, Dobson, Richard J B
Format: Article
Language:English
Subjects:
Care and treatment
Cluster analysis
Computer and Information Sciences
Data processing
Datasets
Emergency Service, Hospital - organization & administration
Flow
Graph theory
Health care
Health informatics
Hospital patients
Hospitals, Public
Medicine and Health Sciences
Mortality
Network analysis
Neurosciences
Patients
Physical Sciences
Queuing theory
Research and Analysis Methods
State Medicine
Topology
Transformation
United Kingdom
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Summary: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.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0185912