Analyzing the queuing theory at the emergency department at King Hussein cancer center

Author:

Qandeel Mahmoud SalamehORCID,Al-Qudah Islam KhaleelORCID,Nayfeh RiyadORCID,Aryan HaithamORCID,Ajaj OmarORCID,Alkhatib HishamORCID,Hamdan YousefORCID

Abstract

Abstract Objectives This study was conducted in 2022 at King Hussein Cancer Center (KHCC) to analyze the queuing theory approach at the Emergency Department (ED) to estimate patients’ wait times and predict the accuracy of the queuing theory approach. Methods According to the statistics, the peak months were July and August, with peak hours from 10 a.m. until 6 p.m. The study sample was a week in July 2022, during the peak days and hours. This study measured patients’ wait times at these three stations: the health informatics desk, triage room, and emergency bed area. Results The average number of patients in line at the health informatics desk was not more than 3, and the waiting time was between 1 and 4 min. Since patients were receiving the service immediately in the triage room, there was no waiting time or line because the nurse’s role ended after taking the vital signs and rating the patient’s disease acuity. Using equations of queuing theory and other relativistic equations in the emergency bed area gave different results. The queuing theory approach showed that the average residence time in the system was between 4 and 10 min. Conclusions Conversely, relativistic equations (ratios of served patients and departed patients and other related variables) demonstrated that the average residence time was between 21 and 36 min.

Funder

King Hussein Cancer Center,Jordan

Publisher

Springer Science and Business Media LLC

Subject

Emergency Medicine

Reference22 articles.

1. Fomundam S, Herrmann J. A survey of queuing theory applications in healthcare. ISR Tech Rep. 2007;24:1–22.

2. Broyles JR, Cochran JK. Estimating business loss to a hospital emergency department from patient reneging by queuing-based regression. IIE Annual Conference. Proceedings. 2007. p. 613–8. https://www.proquest.com/scholarly-journals/estimating-business-loss-hospital-emergency/docview/192455747/se-2.

3. Al-Qahtani S, Alsultan A, Haddad S, Alsaawi A, Alshehri M, Alsolamy S, et al. The association of duration of boarding in the emergency room and the outcome of patients admitted to the intensive care unit. BMC Emerg Med. 2017;17(1):1–6.

4. Hoyer C, Stein P, Alonso A, Platten M, Szabo K. Uncompleted emergency department care and discharge against medical advice in patients with neurological complaints: a chart review. BMC Emerg Med. 2019;19(1):1–8.

5. Cochran JK, Roche KT. A multi-class queuing network analysis methodology for improving hospital emergency department performance. Comput Oper Res. 2009;36(5):1497–512.

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