Discrete-Event Simulation Modeling in Healthcare: A Comprehensive Review

Abstract

Discrete-event simulation (DES) is a stochastic modeling approach widely used to address dynamic and complex systems, such as healthcare. In this review, academic databases were systematically searched to identify 231 papers focused on DES modeling in healthcare. These studies were sorted by year, approach, healthcare setting, outcome, provenance, and software use. Among the surveys, conceptual/theoretical studies, reviews, and case studies, it was found that almost two-thirds of the theoretical articles discuss models that include DES along with other analytical techniques, such as optimization and lean/six sigma, and one-third of the applications were carried out in more than one healthcare setting, with emergency departments being the most popular. Moreover, half of the applications seek to improve time- and efficiency-related metrics, and one-third of all papers use hybrid models. Finally, the most popular DES software is Arena and Simul8. Overall, there is an increasing trend towards using DES in healthcare to address issues at an operational level, yet less than 10% of DES applications present actual implementations following the modeling stage. Thus, future research should focus on the implementation of the models to assess their impact on healthcare processes, patients, and, possibly, their clinical value. Other areas are DES studies that emphasize their methodological formulation, as well as the development of frameworks for hybrid models.

Discrete Event Simulation for Clinic Design

“…The planning, design, and construction of multi-department outpatient healthcare facilities are complicated, expensive, and complicated (al Hroub et al, 2019;Suhaimi et al, 2018;Vahdat et al, 2019). These facilities include multiple stakeholder processes, including physicians, nurses, nurse assistants, and patients (Hong et al, 2013;Suhaimi et al, 2018;Vahdat et al, 2019;Vázquez-Serrano et al, 2021). Further, appointment types, availability of resources, and patient arrival times impact the outpatient delivery systems (Suhaimi et al, 2018;Vahdat et al, 2019).…”

“…Given the complexity of mulita-department outpatient healthcare facility operations, Discrete Event simulation (DES) is considered an effective tool for evaluating resource pooling, performance outcomes, resource utilization, capacity planning, and turnaround times across different situations (al Hroub et al, 2019;Hong et al, 2013;Vahdat et al, 2018Vahdat et al, , 2019Vanberkel et al, 2012;Vázquez-Serrano et al, 2021). Through the DES evaluation, the design strategies for multi-department clinic design are integrated with critical components of operations to achieve higher-quality patient-centered care outcomes (Cai & Jia, 2019;Vahdat et al, 2018).…”

“…Additionally, some models attempt to optimize planning resources by making a future forecast for the quantity, deficiency, or efficient adjacency of architectural spaces (Hong et al, 2013;Mielczarek & Uziałko-Mydlikowska, 2012;Suhaimi et al, 2018;Vahdat et al, 2018Vahdat et al, , 2019Zamani, 2022). Data entry includes the patient flow patterns, staff activities, number of fundamental rooms, patient acuity, the activities performed, and performance (Hong et al, 2013;Suhaimi et al, 2018;Vázquez-Serrano et al, 2021;Zamani, 2022).…”

An exploratory assessment of the impact of a novel risk assessment test on breast cancer clinic waiting times and workflow: a discrete event simulation model

“…staff, rooms, devices), the possibility of queues forming in the system, leading to patient delays, can also be captured. Whilst predominantly used in the context of manufacturing and engineering, the use of DES in healthcare has been rising – with common applications including systems operation research, and disease progression modelling [ 13 , 14 ]. In the context of breast cancer services, most DES applications to date have focused on identifying optimal timings and/or technologies for breast cancer screening programs, without consideration of capacity constraints [ 15 – 19 ].…”

An exploratory assessment of the impact of a novel risk assessment test on breast cancer clinic waiting times and workflow: a discrete event simulation model

“…staff, rooms, devices), the possibility of queues forming in the system, leading to patient delays, can also be captured. Whilst predominantly used in the context of manufacturing and engineering, the use of DES in healthcare has been rising -with common applications including systems operation research, and disease progression modelling (13,14). In the context of breast cancer services, most DES applications to date have focused on identifying optimal timings and/or technologies for breast cancer screening programs, without consideration of capacity constraints (15)(16)(17)(18)(19).…”