Discrete event simulation and data envelopment analysis models for selecting the best resource allocation alternative at an emergency department’s green zone

• Main Authors: Nazhatul Sahima Mohd Yusoff,, Choong, Yeun Liong, Abu Yazid Md Noh,, Wan Rosmanira Ismail,
• Format: Article
• Language: English
• Published: Penerbit Universiti Kebangsaan Malaysia 2018
• Online Access: http://journalarticle.ukm.my/12674/1/35%20Nazhatul%20Sahima.pdf; http://journalarticle.ukm.my/12674/; http://www.ukm.my/jsm/malay_journals/jilid47bil11_2018/KandunganJilid47Bil11_2018.html

The Green Zone of Emergency Department Hospital Universiti Sains Malaysia (EDHUSM) which provides treatment for non-critical cases contributes partly to the hustle and bustle in the emergency department. The imbalance of doctors and nurses with the patient ratio which forms the resources’ bottleneck further results to the long patients’ waiting time especially after the office hours and during weekends and public holidays. Collectively, this disproportion and bottlenecks roots up the current problem faced by Green Zone EDHUSM which constantly fails to achieve the KPIs set by the hospital. Henceforth, this study focuses on the best resource allocation of doctors and nurses for shifts during the weekdays and for shifts during weekends and public holidays. The hybrid method of Discrete Event Simulation, and Data Envelopment Analysis models such as BCC-input oriented and Super-Efficiency, were deployed to obtain the best resource allocation for the two groups of shift. The method produced a series of resources allocation alternatives for doctors and nurses with a total of 64 alternatives for weekdays and 729 alternatives for weekends and public holidays. The results show that the best allocation for doctors and nurses during weekdays are three doctors and three nurses serving for every shift, while during weekends and public holidays, a combination of four doctors and four nurses for every shift are the best. The proposed combinations have reduced the average waiting time, optimized the utilization of doctors and nurses, and managed to increase the number of patients served during weekdays, weekends and public holidays.