Archives

期刊全文

A Hybrid Data Mining Approach for Generalizing Characteristics of Emergency Department Visits Causing Overcrowding

Volume / Date

 VOL. 17 (NO. 1) / JUN.2019

Page

 pp. 43843

DOI

 10.6182/jlis.201906_17(1).001

Language

 English

Yen-Yi Feng, I-Chin Wu*, Tzu-Li Chen, Wen-Han Chang

* Corresponding Author

Yen-Yi Feng Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
I-Chin Wu* Graduate Institute of Library and Information Studies, School of Learning Informatics, National Taiwan Normal University, Taipei, Taiwan
icwu@ntnu.edu.tw
Tzu-Li Chen Department of Information Management, Fu-Jen Catholic University, Taipei, Taiwan
Wen-Han Chang Emergency Medicine, Mackay Memorial Hospital, Taipei, Taiwan
Department of Medicine, Mackay Medical College, New Taipei, Taiwan
Mackay Medicine, Nursing and Management College, Taipei, Taiwan
Graduate Institute of Injury Prevention and Control, College of Public Health and Nutrition, Taipei Medical University, Taipei, Taiwan
Department of Emergency, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
Institute of Mechatronic Engineering, National Taipei University of Technology, Taipei, Taiwan

Abstract

Hospital emergency department (ED) crowding has led to an increase in patient waiting times; solving this problem requires a better understanding of the patient behavior. In this work, we adopt decision tree analysis which facilitate the interpretation and understanding of ED visits at Mackay Memorial Hospital, a representative ED in our country. Accordingly, a hybrid data mining approach is proposed to predict patients' length of stay (LOS) and explain their associated characteristics under various LOSs, especially for frequent and non-urgent groups with shorter stays in the ED. With two datasets from the first half-years of 2009 and 2010 containing 40,849 and 43,708 records respectively, we verify the stability and robustness of the proposed approach. We confirm the qualified rules based on patient characteristics and treatment information extracted by the decision tree induction method for the patient population that primarily causes ED overcrowding—patients with non-urgent conditions and short ED stays—in terms of accuracy, medical clinical value and relatedness. We identify that patients with short LOSs demonstrated similar characteristics in visiting ED. We also identify that attributes such as treatment frequencies of laboratory testing, age, and mode of arrival are good indicators for predicting patients’ LOSs. The results clarify ED crowding in Taiwan and can guide investigations of ED overcrowding from the perspective of generalizing characteristics of visits. The results serve as a reference model for related ED research in a similar context for clinical decision support.

Keywords

Clinical Decision Support; ED Overcrowding; Hybrid Data Mining; Length of Stay; Patient Characteristics