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

doi:10.3233/SHTI190203

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

Emergency Medicine

Research output: Chapter in Book/Report/Conference proceeding › Conference 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 language	English (US)
Title of host publication	MEDINFO 2019
Subtitle of host publication	Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics
Editors	Brigitte Seroussi, Lucila Ohno-Machado, Lucila Ohno-Machado, Brigitte Seroussi
Publisher	IOS Press
Pages	158-162
Number of pages	5
ISBN (Electronic)	9781643680026
DOIs	https://doi.org/10.3233/SHTI190203
State	Published - Aug 21 2019
Event	17th World Congress on Medical and Health Informatics, MEDINFO 2019 - Lyon, France Duration: Aug 25 2019 → Aug 30 2019

Publication series

Name	Studies in Health Technology and Informatics
Volume	264
ISSN (Print)	0926-9630
ISSN (Electronic)	1879-8365

Conference

Conference	17th World Congress on Medical and Health Informatics, MEDINFO 2019
Country/Territory	France
City	Lyon
Period	8/25/19 → 8/30/19

Keywords

Cluster Analysis
Data Mining
Hospital Emergency Service

ASJC Scopus subject areas

Biomedical Engineering
Health Informatics
Health Information Management

Access to Document

10.3233/SHTI190203

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

Characterizing frequent flyers of an emergency department using cluster analysis. / Shehada, Emile Ramez; He, Lu; Eikey, Elizabeth V.; Jen, Maxwell; Wong, Andrew; Young, Sean D.; Zheng, Kai.

MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. ed. / Brigitte Seroussi; Lucila Ohno-Machado; Lucila Ohno-Machado; Brigitte Seroussi. IOS Press, 2019. p. 158-162 (Studies in Health Technology and Informatics; Vol. 264).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Shehada, ER, He, L, Eikey, EV, Jen, M, Wong, A, Young, SD & 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. Studies in Health Technology and Informatics, vol. 264, IOS Press, pp. 158-162, 17th World Congress on Medical and Health Informatics, MEDINFO 2019, Lyon, France, 8/25/19. https://doi.org/10.3233/SHTI190203

Shehada ER, He L, Eikey EV, Jen M, Wong A, Young SD et al. Characterizing frequent flyers of an emergency department using cluster analysis. In Seroussi B, Ohno-Machado L, Ohno-Machado L, Seroussi B, editors, MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. IOS Press. 2019. p. 158-162. (Studies in Health Technology and Informatics). https://doi.org/10.3233/SHTI190203

Shehada, Emile Ramez ; He, Lu ; Eikey, Elizabeth V. ; Jen, Maxwell ; Wong, Andrew ; Young, Sean D. ; Zheng, Kai. / Characterizing frequent flyers of an emergency department using cluster analysis. MEDINFO 2019: Health and Wellbeing e-Networks for All - Proceedings of the 17th World Congress on Medical and Health Informatics. editor / Brigitte Seroussi ; Lucila Ohno-Machado ; Lucila Ohno-Machado ; Brigitte Seroussi. IOS Press, 2019. pp. 158-162 (Studies in Health Technology and Informatics).

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AU - He, Lu

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AU - Zheng, Kai

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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.

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KW - Hospital Emergency Service

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ER -

Characterizing frequent flyers of an emergency department using cluster analysis

Abstract

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Conference

Keywords

ASJC Scopus subject areas

Access to Document

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