Managing Overcrowding In Healthcare Using Fuzzy Logic

Keywords

Emergency Department; Expert Knowledge; Fuzzy logic; Healthcare; Overcrowding

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 patientdemand, patient complexity, staffing level, clinician workload, and boarding status when defining the crowding level. The hierarchical fuzzy logic approach is utilized to accomplish the goals of this framework by combining a diverse pool of healthcare expert perspectives while addressing the complexity of the overcrowding issue.

Publication Date

1-1-2017

Publication Title

Artificial Intelligence: Advances in Research and Applications

Number of Pages

196-225

Document Type

Article; Book Chapter

Personal Identifier

scopus

Socpus ID

85044679835 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85044679835

This document is currently not available here.

Share

COinS