Impact of an approach combining optimization and simulation to improve the service quality of an emergency department

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Published Sep 27, 2023
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Vol. 101 No. 4 (2023): Tunis Med apr 2023

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Dorsaf Daldoul
University of Tunis El Manar, National Engineering School of Tunis, LR11ES20 LACCS Laboratory, Tunis, Tunisia / University of Artois, UR 3926 LGI2A Laboratory, F-62400 Bethune, France
Issam Nouaouri
University of Artois, UR 3926 LGI2A Laboratory, F-62400 Bethune, France
Hanen Bouchriba
University of Tunis El Manar, National Engineering School of Tunis, LR11ES20 LACCS Laboratory, 1002 Tunis, Tunisia / University of Artois, UR 3926 LGI2A Laboratory, F-62400 Bethune, France
Hamid Allaoui
University of Artois, UR 3926 LGI2A Laboratory, F-62400 Bethune, France

Abstract

Introduction: Emergency Department (ED) represents a complex system due to its limited resources and random patient arrivals. It is often saturated by a continuous flow of patients, which causes an excessive wait time and length of stay.


Aim: To evaluate the impact of the proposed combining approach (optimization with simulation) on improving the performance of an emergency department in terms of patients’ length of stay and resources utilization balanced (physicians and nurses).


Methods: This study integrates simulation with optimization to design a planning decision support for an emergency department. First, we used a stochastic mixed integer linear programing (MILP) to find the optimal number of staff and beds while considering the uncertainties on the patients’ arrival and service times. Next, we construct a Discrete Event Simulation (DES) model to analyse and evaluate this resources allocation as well as different patient scheduling rules in order to minimize the length of stay and to balance resources utilization.


Results: The approach is applied to a case study at a Tunisian emergency department. Our analysis indicates that the proposed approach generates the best resources allocation that would significantly reduce the length of stay with an average of 44.55 % and balance the resources utilization.


Conclusion: This approach allows helping decision makers to improve the service quality of the ED via key performance indicators. DES and MILP prove to be an effective tool for studying the effects of different scenarios to optimize capacity allocation and to minimize patients’ length of stay.

Keywords:

management of emergency department, stochastic planning, discrete event simulation, length of stay, resource utilization

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