Can a system dynamics model of the emergency department show which levers reduce bottlenecks and delays to improve access to care?. (19th January 2020)

• Record Type: Journal Article
• Authors: McAvoy, Sue; Staib, Andrew; Treston, Gregory
• Is Part Of: Systems research and behavioral science. Volume 38:Number 1(2021)
• Page Start: 61
• Page End: 79
• Publication Date: 2020-01-19
• Subjects: emergency department -- patient flow -- simulation -- system dynamics; Psychology -- Periodicals; Social sciences -- Periodicals; System theory -- Periodicals; 003
• DOI: 10.1002/sres.2663
• Languages: English
• ISSNs: 1092-7026

Abstract

Abstract: The purpose of this paper is to demonstrate through practical application how a system dynamics (SD) patient flow model of an emergency department (ED) can show which levers effectively reduce backlogs to improve access to care. Overcrowded EDs are struggling to meet demand and access targets. In 2016 and 2017, in the UK and Australia, respectively, 15% and 28% of arrivals waited longer than the targeted 4‐hr treatment time. Historically, simulation models that have informed access to emergency care have ignored the wider systems impacts. There is a growing awareness of the value of systems analysis tools for informing interventions and policy. In this study, we constructed a pilot system dynamics patient flow model, where the scope was the ambulatory and ambulance patient arrivals, the ED processes for acute and fast‐track pathways, pathology and radiology services, the ED short‐stay unit, and the Medical Assessment Planning Inpatient Unit. Patients queued to access constrained ED resources (doctors and beds) and diagnostic services (pathology and X‐ray). The model was tested on actual data from five separate historical periods spanning 3 years. The resultant daily pattern of peaks and troughs in patient flow and system delays accurately replicated patterns in actual patient flows, resource use, and the location of delays. "What if" scenario analysis (b) simulated how access would have looked in the sample weeks with different intervention strategies, (b) simulated system limits on the basis of current resources, (c) accurately identified levers that historically have been most effective at minimizing ambulance ramping, and (d) identified when additional staffing would fail to improve flow.