NDLI: Deciding the minimum staffing level in an emergency medical center during sandstorms

Content Provider	IEEE Xplore Digital Library
Author	Al-Ajeel, F. Al-Thuwaini, L. Al-Qallaf, M. Al-Faraj, N. Al-Muzayan, S. Joumaa, C.
Copyright Year	2015
Description	Author affiliation: Dept. of Ind. Eng., American Univ. of the Middle East, Egaila, Kuwait (Al-Ajeel, F.; Al-Thuwaini, L.; Al-Qallaf, M.; Al-Faraj, N.; Al-Muzayan, S.) \|\| Dept. of Comput. Eng., American Univ. of the Middle East, Egaila, Kuwait (Joumaa, C.)
Abstract	This paper presents a case study of a discreteevent simulation (DES) model of an emergency department (ED) unit in a governmental hospital in Kuwait. The emergency department operations were modeled using Arena software. The simulation model was reinforced through verification and validation. Some scenarios in an attempt to determine the minimum staff level were evaluated. These scenarios show that major improvement in patient waiting time can be obtained by minor changes in the number of resources. These changes reduced the waiting times of patients by 29% in the Female Observation, by 62% in the Male Observation, by 15% in the first Examination Room, and by 23% in the second Examination Room. The relationship between sandstorm occurrences and the number of visits to the emergency department was also investigated using correlation and regression analysis. The result of the analysis was that there is a strong linear relationship between sandstorm occurrences and the number of visits to the emergency department.
Starting Page	711
Ending Page	716
File Size	1737382
Page Count	6
File Format	PDF
e-ISBN	9781467380669
DOI	10.1109/IEEM.2015.7385740
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-12-06
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Analytical models Correlation Emergency departments Storms Hospitals Computational modeling correlation regression discrete-event simulation Software healthcare modeling sandstorms
Content Type	Text
Resource Type	Article

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