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

Daniel M. Bean, Clive Stringer, Neeraj Beeknoo, James Teo, Richard J. B. Dobson

Research output: Contribution to journalArticlepeer-review

18 Citations (Scopus)
203 Downloads (Pure)


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.
Original languageEnglish
Pages (from-to)1-16
JournalPLOS One
Issue number10
Early online date2 Oct 2017
Publication statusPublished - 2017


  • Network analysis
  • patient flow
  • Graph Theory
  • Health informatics


Dive into the research topics of 'Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance'. Together they form a unique fingerprint.

Cite this