TY - GEN
T1 - Characterizing frequent flyers of an emergency department using cluster analysis
AU - Shehada, Emile Ramez
AU - He, Lu
AU - Eikey, Elizabeth V.
AU - Jen, Maxwell
AU - Wong, Andrew
AU - Young, Sean D.
AU - Zheng, Kai
PY - 2019/8/21
Y1 - 2019/8/21
N2 - Emergency department (ED) overcrowding has been a pain point in hospitals across the globe. “Frequent flyers,” who visited the ED at a much higher rate than average, account for almost one third of ED visits even though they represent only a small proportion of all ED patients. In this study, we used data-mining methods to cluster ED frequent flyers at a large academic medical center in the US. The objective was to identify distinct types of frequent flyers, and the common characteristics associated with each type. The results show that the frequent flyers at the ED have three subgroups each exhibiting distinct characteristics: (1) the elderly with chronic health conditions, (2) middle-aged males with unhealthy behavior, and (3) adult females who are generally healthy. These findings may inform targeted interventional strategies for patients of each subgroup, who likely have distinct reasons for visiting the ED frequently, to reduce ED overcrowding.
AB - Emergency department (ED) overcrowding has been a pain point in hospitals across the globe. “Frequent flyers,” who visited the ED at a much higher rate than average, account for almost one third of ED visits even though they represent only a small proportion of all ED patients. In this study, we used data-mining methods to cluster ED frequent flyers at a large academic medical center in the US. The objective was to identify distinct types of frequent flyers, and the common characteristics associated with each type. The results show that the frequent flyers at the ED have three subgroups each exhibiting distinct characteristics: (1) the elderly with chronic health conditions, (2) middle-aged males with unhealthy behavior, and (3) adult females who are generally healthy. These findings may inform targeted interventional strategies for patients of each subgroup, who likely have distinct reasons for visiting the ED frequently, to reduce ED overcrowding.
KW - Cluster Analysis
KW - Data Mining
KW - Hospital Emergency Service
UR - http://www.scopus.com/inward/record.url?scp=85071413878&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85071413878&partnerID=8YFLogxK
U2 - 10.3233/SHTI190203
DO - 10.3233/SHTI190203
M3 - Conference contribution
C2 - 31437905
AN - SCOPUS:85071413878
T3 - Studies in Health Technology and Informatics
SP - 158
EP - 162
BT - MEDINFO 2019
A2 - Seroussi, Brigitte
A2 - Ohno-Machado, Lucila
A2 - Ohno-Machado, Lucila
A2 - Seroussi, Brigitte
PB - IOS Press
T2 - 17th World Congress on Medical and Health Informatics, MEDINFO 2019
Y2 - 25 August 2019 through 30 August 2019
ER -