Characterizing frequent flyers of an emergency department using cluster analysis

Emile Ramez Shehada, Lu He, Elizabeth V. Eikey, Maxwell Jen, Andrew Wong, Sean D. Young, Kai Zheng

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationMEDINFO 2019
Subtitle of host publicationHealth and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
EditorsBrigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
PublisherIOS Press
Pages158-162
Number of pages5
ISBN (Electronic)9781643680026
DOIs
StatePublished - Aug 21 2019
Event17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France
Duration: Aug 25 2019Aug 30 2019

Publication series

NameStudies in Health Technology and Informatics
Volume264
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference17th World Congress on Medical and Health Informatics, MEDINFO 2019
CountryFrance
CityLyon
Period8/25/198/30/19

Keywords

  • Cluster Analysis
  • Data Mining
  • Hospital Emergency Service

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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  • Cite this

    Shehada, E. R., He, L., Eikey, E. V., Jen, M., Wong, A., Young, S. D., & Zheng, K. (2019). Characterizing frequent flyers of an emergency department using cluster analysis. In B. Seroussi, L. Ohno-Machado, L. Ohno-Machado, & B. Seroussi (Eds.), MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics (pp. 158-162). (Studies in Health Technology and Informatics; Vol. 264). IOS Press. https://doi.org/10.3233/SHTI190203