PT - JOURNAL ARTICLE AU - Daniel M. Bean AU - Clive Stringer AU - Neeraj Beeknoo AU - James Teo AU - Richard J. B. Dobson TI - Network analysis of patient flow in two UK acute care hospitals identifies key sub-networks for A&E performance AID - 10.1101/120188 DP - 2017 Jan 01 TA - bioRxiv PG - 120188 4099 - http://biorxiv.org/content/early/2017/03/24/120188.1.short 4100 - http://biorxiv.org/content/early/2017/03/24/120188.1.full AB - 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 the following day. 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.