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

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