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)

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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• 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
• Publisher: Elsevier
• ISSN: 0925-7535
• 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

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
67960	1	Data integration to support quality in acute care	Alastair Ross	The Health Foundation (HEALFOU)	N/A	SM - DENTAL SCHOOL