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

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

doi:10.1371/journal.pone.0185912

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.

Highlights

Patient flow through a hospital is a key determinant of efficient hospital functioning
Previous studies have focused on linear flow processes, queuing and single patient pathways [1,2] failing to capture the complex topology of a real-world hospital comprising hundreds of patient journeys overlapping in time and space
A&E waiting time is thought to be partly determined by the occupancy and patient flow in downstream wards, so we considered the hypothesis that changes in the structure of the hospital network over time are correlated with changes in performance

Summary

Introduction

Patient flow through a hospital is a key determinant of efficient hospital functioning. We analysed the topology of patient flow through hospital wards at two of King’s major hospital sites modelling this as a directed flow network (Fig 1). Accident and Emergency department (A&E) performance against the UK’s 4-hour waiting time target is a key metric used to assess hospitals, and patient flow through the hospital has been attributed to be a contributing factor [5]. To understand this relationship, we performed a data-driven analysis of the major hospital sites of King’s College Hospital NHS Foundation Trust to compare network flow during periods of high performance and low performance. We proposed that graph and cluster analysis would be able to identify (1) patterns of patient flow associated with good or poor A&E performance, and (2) cyclical patterns of patient flow on weekday and weekends

Methods

Ethical approval

Results

Findings

Discussion