An integrated statistical model of Emergency Department length of stay informed by Resilient Health Care principles

Alastair J. Ross*, Trevor Murrells, Tom Kirby, P. Jaye, Janet E. Anderson

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
71 Downloads (Pure)

Abstract

Background: Hospital Emergency Departments (EDs) face variable demand and capacity issues affecting timely discharge of patients. This is due in part to a lack of integration of routine monitoring data, affecting anticipation and response. Methods: Patient flow was modelled (four hour target breaches; time to decision-to-admit; subsequent time to admit-to-hospital) in a busy ED. Patient and organisational data were collated, screened and conceptualised using Resilient Health Care (RHC) theory. Data were collected for all patients presenting during a 24-month period (May 2014–April 2016; n = 232,920) and analysed via multivariable logistic regression for four hour target breaches, and ordinary least squares regression for time. A measure of effect size was calculated for each independent variable. Overall model fit was assessed using percent concordant. Results: Length of stay is related to demand, capacity and process indicators including: number of patients; night shift; first location being resuscitation or major injury area(s); urgent or very urgent triage patients; patients readmitting from up to 7 days previous; bed capacity; recent ambulance arrivals; and patients where the primary presenting complaint (PPC) is related to mental health or difficult to ascertain. Conclusions: Understanding variation in performance through RHC theory can support staff and organisations in monitoring, anticipating and responding. A set of reliable core predictors has been identified to help design future ways to facilitate resilient performance through early indicators of pressure.

Original languageEnglish
Pages (from-to)129-136
Number of pages8
JournalSAFETY SCIENCE
Volume120
Early online date3 Jul 2019
DOIs
Publication statusPublished - 1 Dec 2019

Keywords

  • Data systems
  • Emergency department
  • Informatics
  • Patient flow
  • Resilient Health Care

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