Characterizing Frequent Flyers of an Emergency Department Using Cluster Analysis.
Cluster Analysis
Data Mining
Hospital Emergency Service
Journal
Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582
Informations de publication
Date de publication:
21 Aug 2019
21 Aug 2019
Historique:
entrez:
24
8
2019
pubmed:
24
8
2019
medline:
11
9
2019
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 31437905
pii: SHTI190203
doi: 10.3233/SHTI190203
doi:
Types de publication
Journal Article
Langues
eng