Leveraging discrete event simulation modeling to evaluate design and process improvements of an emergency department

Abstract

This study exemplifies the practical application of the Discrete Event Simulation (DES) approach for evaluating the effectiveness of suggested processes and design modifications in improving the existing bottlenecks of an Emergency Department. EDs are under escalating pressure to deliver efficient care while handling considerable challenges, such as overcrowding, delays, length of stay, safety risks, or staffing. Many ED appointments are non-urgent and can be treated in an alternative outpatient setting. Suitable demand-capacity matching and adjusted admission protocols reduce ED patients' Length of Stay (LOS) and improve boarding times. Alternatively, new design suggestions include applying results-pending areas where lower acuity patients wait for their pending lab or imaging results. In this study, DES assesses underlying conditions and existing bottlenecks in an existing ED. The current ED flow involved a "pull-until-full" for exam room boarding and bedside registration after triage fulfillment. Nonetheless, the ED experienced boarding delays for patients waiting to be admitted into the hospital. This study explored two scenarios in DES as potential alternatives for reducing LOS: the implication of a "rapid-admit" protocol and a "results-pending" area. Findings showed that the Rapid-Admit process reduced the admitted patient's LOS by 16%. On average, the results-pending implication reduced the admit LOS by an average of 32% across all ESI levels. These findings suggest the importance of process, staffing, and spatial modifications to achieve ED operational improvements. DES enabled a data-driven approach to evaluate bottlenecks, enhance architect-owner communication, and optimize the system for future design and process improvement alternatives.

Discrete Event Simulation for Clinic Design

“…It also contributed to defining reasonable and achievable strategies for pooled resources across the departments. Besides, the graphic and video outputs of the simulation model improved communication and decision-making for stakeholders and facilitated model validation, which has been noted in prior studies (Cai & Jia, 2019;Vahdat et al, 2018Vahdat et al, , 2019Zamani, 2022).…”

“…Over the last 40 years, DES models have been growing in popularity and have been accepted by healthcare administrators as practical tools for resource allocations that improve patient flow and satisfaction (Cai & Jia, 2019;Fone et al, 2003;Mielczarek & Uziałko-Mydlikowska, 2012;Norouzzadeh et al, 2015;Rohleder et al, 2007;Swain, 2011). DES facilitates decision-making and empowers healthcare planners by analyzing the impact of various feasible alternatives (Swain, 2011;Zamani, 2022;Zeigler et al, 2000). DES is particularly advantageous in determining the impact of planning or expanding healthcare centers by accounting for various resources, stakeholder needs, clinical rooms, and patient volume (Brailsford & Hilton, 2001;Cai & Jia, 2019;Mielczarek & Uziałko-Mydlikowska, 2012).…”

“…Applications include reengineering patient flow for reduced waiting time or improving staff scheduling (Mielczarek & Uziałko-Mydlikowska, 2012;Suhaimi et al, 2018;Swain, 2011;Vahdat et al, 2018Vahdat et al, , 2019. 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).…”

“…Resource allocation is a popular application for simulation modeling of simulation modeling is to determine appropriate staffing levels (Santibáñez et al, 2009;Vahdat et al, 2018;Vanberkel et al, 2012;Wischik et al, 2008). Another practical usage of DES is to evaluate the impact of planning and volume changes on the future capacity requirement, efficient design, and staffing needs on operational outcomes (Cai & Jia, 2019;Mielczarek & Uziałko-Mydlikowska, 2012;Rohleder et al, 2007;Santibáñez et al, 2009;Swain, 2011;Vahdat et al, 2019;Zamani, 2022;Zeigler et al, 2000).…”

“…The simulation scenario suggested pod configuration and pooling exam rooms across physicians reduce the number of needed exam rooms by 22.3%. Through the application of DES, Zamani's (2022) study proposed and addition of a results-pending room for a future Emergency Department (ED) layout that potentially reduced the admitted patient's Length of stay (LOS) by 32%.…”