Characterizing Frequent Flyers of an Emergency Department Using Cluster Analysis.


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
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

Pagination

158-162

Auteurs

Emile Ramez Shehada (ER)

Department of Informatics, University of California, Irvine, Irvine, CA, USA.

Lu He (L)

Department of Informatics, University of California, Irvine, Irvine, CA, USA.

Elizabeth V Eikey (EV)

Department of Informatics, University of California, Irvine, Irvine, CA, USA.

Maxwell Jen (M)

Department of Emergency Medicine University of California, Irvine, Irvine, CA, USA.

Andrew Wong (A)

Emergency Department, University of California, Davis, Davis, CA, USA.

Sean D Young (SD)

Department of Family Medicine, University of California, Los Angeles, Los Angeles, CA, USA.
University of California Institute for Prediction Technology, Los Angeles, CA, USA.

Kai Zheng (K)

Department of Informatics, University of California, Irvine, Irvine, CA, USA.

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Classifications MeSH