Patterns in Emergency-Department Arrivals and Length of Stay: Input for Visualizations of Crowding
Identifiers and Pagination:Year: 2016
First Page: 1
Last Page: 14
Publisher Id: TOERGJ-9-1
Article History:Received Date: 19/05/2015
Revision Received Date: 12/11/2016
Acceptance Date: 30/03/2016
Electronic publication date: 13/05/2016
Collection year: 2016
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution-Non-Commercial 4.0 International Public License (CC BY-NC 4.0) (https://creativecommons.org/licenses/by-nc/4.0/legalcode), which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Crowding is common in emergency departments (EDs) and increases the risk of medical errors, patient dissatisfaction, and clinician stress. The aim of this study is to investigate patterns in patient visits and bottlenecks in ED work in order to discuss the prospects of visualizing such patterns to help manage crowding. We analyze two years of data from a Danish ED for patterns in the patient visits and interview six clinicians from the ED about bottlenecks in their work. The hour of the day explains 50% of the variance in the number of patient arrivals. In addition, there are weekly and yearly patterns in patient arrivals. With respect to the flow of patients through the ED, length of stay increases from low to medium triage levels and then decreases from medium to high triage levels. Also, length of stay increases with patient age. The bottlenecks in the work in the ED relate to patient input (mornings, boom days), patient throughput (staff work hours, linear workflows, manual data entry, overview of patient progress, personal competences), and patient output (no admissions at night, scheduling patient transfers, home transports). The patterns in patient arrivals and length of stay capture factors important to the evolving balance between the demand for ED services and the available resources. Visualization of the patterns, thus, appears a promising tool in managing ED crowding. However, visualizations presuppose reliable data and are expected by the clinicians to be accurate and prognostic. We propose three visualizations.