An agent-based model for quantitatively analyzing and predicting the complex behavior of emergency departments
Authored by Francisco Epelde, Zhengchun Liu, Dolores Rexachs, Emilio Luque
Date Published: 2017
DOI: 10.1016/j.jocs.2017.05.015
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Abstract
Hospital based emergency departments (EDs) are highly integrated service
units devoted primarily to handling the needs of patients arriving
without prior appointment, and with uncertain conditions. In this
context, analysis and management of patient flows play a key role in
developing policies and decisions for overall performance improvement.
However, patient flows in EDs are considered to be very complex because
of the different pathways patients may take and the inherent uncertainty
and variability of healthcare processes. The agent-based model provides
a flexible platform for studying ED operations, as it predicts the
system-level behavior from individual level interactions. In this way,
policies such as staffing can be changed and the effect on system
performance, such as waiting times and throughput, can be quantified.
The overall goal of this study is to develop tools to better understand
the complexity, evaluate policy and improve efficiencies of ED units.
The main contribution of this paper includes: an agent-based model of
ED, a flexible atomic data monitoring layer for agent state tracing, and
a master/worker based framework for efficiently executing the model and
analyzing simulation data. The presented model has been calibrated to
imitate a real ED in Spain, the simulatipn results have proven the
feasibility of using agent-based model to study ED system. (C) 2017
Elsevier B.V. All rights reserved.
Tags
Agent-based model
Simulation
Performance
Optimization
Decision support system
complex adaptive system
Methodology
Decision-support-system
Emergency department
Leave
Length-of-stay
Health-care-delivery