Page 211 - Data Science Algorithms in a Week

In: Artificial Intelligence ISBN: 978-1-53612-677-8
Editors: L. Rabelo, S. Bhide and E. Gutierrez © 2018 Nova Science Publishers, Inc.

Chapter 9

MANAGING OVERCROWDING IN HEALTHCARE

USING FUZZY LOGIC

2
1,*
Abdulrahman Albar , Ahmad Elshennawy ,
Mohammed Basingab and Haitham Bahaitham 4
3
1 Industrial Engineering, Jazan University, Jazan, Saudi Arabia
2 Industrial Engineering & Management Systems, University of Central Florida,
Orlando, Florida, US
3 Industrial Engineering, King Abdulaziz University, Jeddah, Saudi Arabia
4 Industrial Engineering, King Abdulaziz University, Rabigh, Saudi Arabia

ABSTRACT

Emergency Departments (EDs) represent a crucial component of any healthcare
infrastructure. In today’s world, healthcare systems face growing challenges in delivering
efficient and time-sensitive emergency care services to communities. Overcrowding
within EDs represents one of the most significant challenges for healthcare quality.
Research in this area has resulted in creating several ED crowding indices, such as
National Emergency Department Overcrowding Scale (NEDOCS) that have been
developed to provide measures aimed at mitigating overcrowding. Recently, efforts made
by researchers to examine the validity and reproducibility of these indices have shown
that they are not reliable in accurately assessing overcrowding in regions beyond their
original design settings. To overcome the shortcomings of previous indices, the study
presents a novel framework for quantifying and managing overcrowding based on
emulating human reasoning in overcrowding perception. The framework of this study
takes into consideration emergency operational and clinical factors such as patient

* Corresponding Author Email: aalbar@jazanu.edu.sa.