Modelling patient trajectories in emergency department simulations using retrospective patient cohorts
Year: 2024
Type: article
Abstract: 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. • Acuity inadequately characterises patient trajectories in the emergency department. • Retrospective patient indicators improve the accuracy of simulated trajectories. • Validation on retrospective patient indicators makes model evaluation more robust. • Simulations using retrospective information estimate changes in length of stay better.
Institution: The University of Melbourne
Language: English
Subfield: Emergency Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $2,640
Funder: University of Melbourne
OpenAlex ID: W4402563424