Modelling patient trajectories in emergency department simulations using retrospective patient cohorts.

Agent-based modelling Computer simulation Emergency department Length of stay Patient trajectories Process mining

Journal

Computers in biology and medicine
ISSN: 1879-0534
Titre abrégé: Comput Biol Med
Pays: United States
ID NLM: 1250250

Informations de publication

Date de publication:
17 Sep 2024
Historique:
received: 07 05 2024
revised: 20 08 2024
accepted: 08 09 2024
medline: 19 9 2024
pubmed: 19 9 2024
entrez: 18 9 2024
Statut: aheadofprint

Résumé

Computer simulations of emergency departments (EDs) are tools that can support managing and optimising ED operations. A core component of ED simulation models is patient trajectories, defined as the series of activities patients undergo in the ED from arrival to discharge. The combined duration of these activities, and waiting times between them, determines a patient's length of stay (LOS). Patient trajectories are often calibrated and validated solely based on the estimated acuity of patients assigned upon arrival. However, acuity is a prospective patient indicator that inconsistently reflects patients' actual urgency and resource usage as seen retrospectively upon discharge. Here, we propose a data-driven ED simulation model in which patient trajectories are modelled based on both acuity and retrospective patient indicators. We show that including retrospective patient indicators recovers the observed LOS distributions more accurately than when using acuity alone. We also demonstrate how the use of retrospective patient indicators leads to more plausible estimates of the impact of increased stress in the ED on patients' LOS. Our work exemplifies how we can better utilise ED data to make the development and evaluation of ED simulation models more accurate and robust, enabling them to provide more reliable and useful operational insights.

Identifiants

pubmed: 39293336
pii: S0010-4825(24)01232-0
doi: 10.1016/j.compbiomed.2024.109147
pii:
doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

109147

Informations de copyright

Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Auteurs

Roben Delos Reyes (R)

School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia. Electronic address: rdelosreyes@student.unimelb.edu.au.

Daniel Capurro (D)

School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia; Department of Medicine, The University of Melbourne, Parkville, Victoria, Australia.

Nicholas Geard (N)

School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia.

Classifications MeSH