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

Ross, A. , Murrels, T., Kirby, T., Jaye, P. and Anderson, J.E. (2019) An integrated statistical model of Emergency Department length of stay informed by Resilient Health Care principles. Safety Science, 120, pp. 129-136. (doi: 10.1016/j.ssci.2019.06.018)

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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.

Item Type:Articles
Glasgow Author(s) Enlighten ID:Ross, Dr Alastair
Authors: Ross, A., Murrels, T., Kirby, T., Jaye, P., and Anderson, J.E.
College/School:College of Medical Veterinary and Life Sciences > School of Medicine, Dentistry & Nursing > Dental School
Journal Name:Safety Science
ISSN (Online):0925-7535
Published Online:03 July 2019
Copyright Holders:Copyright © 2019 Elsevier
First Published:First published in Safety Science 120:129-136
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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Project CodeAward NoProject NamePrincipal InvestigatorFunder's NameFunder RefLead Dept
679601Data integration to support quality in acute careAlastair RossThe Health Foundation (HEALFOU)N/ASM - DENTAL SCHOOL