Retrospective identification of latent subgroups of emergency department patients: A machine learning approach

Autor: Kalpani I Duwalage, Andrew Wong, Ellen Burkett, M. Helen Thompson, Gentry White
Rok vydání: 2021
Předmět:
Zdroj: Emergency Medicine Australasia. 34:252-262
ISSN: 1742-6723
1742-6731
DOI: 10.1111/1742-6723.13875
Popis: OBJECTIVE This research aims to (i) identify latent subgroups of ED presentations in Australian public EDs using a data-driven approach and (ii) compare clinical, socio-demographic and time-related characteristics of ED presentations broadly using the subgroups. METHODS We examined presentations to four public hospital EDs in Queensland from 2009 to 2014. An unsupervised machine learning algorithm, Clustering Large Applications, was used to cluster ED presentations. RESULTS There were six subgroups common across the EDs, primarily distinguishable by age, and subsequently by triage category, ED length of stay, arrival mode, departure status and several time-related attributes. Around 10% to 30% of the total presentations had high resource utilisation, with half of these from older patients (55+ years). ED resource utilisation per population was highest among the oldest cohort (75+ years). Children and young adults more frequently presented to the ED outside general-practitioner hours, mostly on Sundays. Older persons were more likely to present at any time, rather than specific hours, days or seasons. ED service performance measured against commonly used access-target indicators were rarely satisfied for older people and frequently satisfied for children. CONCLUSION Clustering Large Applications is effective in finding latent groups in large-scale mixed-type data, as demonstrated in the present study. Six types of ED presentations were identified and described using clinically relevant characteristics. The present study provides evidence for policy makers in Australia to develop alternative ED models of care tailored around the care needs of the differing groups of patients and thereby supports the sustainable delivery of acute healthcare.
Databáze: OpenAIRE
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