Retrospective identification of latent subgroups of emergency department patients: A machine learning approach. (6th October 2021)

• Record Type: Journal Article
• Authors: Duwalage, Kalpani I; Burkett, Ellen; White, Gentry; Wong, Andy; Thompson, M Helen
• Is Part Of: Emergency medicine Australasia. Volume 34:Number 2(2022)
• Page Start: 252
• Page End: 262
• Publication Date: 2021-10-06
• Subjects: clustering -- clustering large applications -- emergency department -- presentations -- subgroups; Emergency medicine -- Periodicals; Emergency medicine -- Australasia -- Periodicals; 616.025
• DOI: 10.1111/1742-6723.13875
• Languages: English
• ISSNs: 1742-6731

Abstract

Abstract: 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. Abstract : This research identifies and describes latent subgroups of ED presentations using an unsupervised machine learning algorithm, Clustering Large Applications (CLARA). There were six types of subgroups common across the EDs with varying socio‐demographic, clinical and time‐related characteristics; among them, older presentations were identified as the most vulnerable group in EDs. CLARA is an effective algorithm to find latent groups in large‐scale mixed‐type data.