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2022•Studies in health technology and informaticsOpen access

Exploratory Clustering for Emergency Department Patients

Georgios Feretzakis, Aikaterini Sakagianni, Dimitris Kalles, Evangelos Loupelis, Lazaros Tzelves, Vasileios Panteris, Rea Chatzikyriakou, Νικόλαος Τράκας, Stavroula Kolokytha, Polyxeni Batiani, Zoi Rakopoulou, Aikaterini Tika, Stavroula Petropoulou, Ilias Dalainas, Vasileios Kaldis

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Abstract

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

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What this paper is about

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

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OpenAlex reports 8 citations for this work. Citation counts describe recorded attention and do not establish research quality.

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Available abstract

Emergency department (ED) overcrowding is an increasing global problem raising safety concerns for the patients. Elaborating an effective triage system that properly separates patients requiring hospital admission remains difficult. The objective of this study was to compare a clustering-related technique assignment of emergency department patients with the admission output using the k-means algorithm. Incorporating such a model into triage practice could theoretically shorten waiting times and reduce ED overcrowding.

Key concepts: Overcrowding, Emergency department, Triage, Medical emergency, Medicine, Cluster analysis, Emergency medicine, Computer science

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