Optimising Outpatient Pharmacy Staffing to Minimise Patients Queue Time using Discrete Event Simulation

Abstract

Background: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time. Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario. Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding. Keywords: Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System AnalysisBackground: To remain relevant in the customer-oriented market, hospitals must pay attention to the quality of services and meet customers' expectations from admission to discharge stage. For an outpatient customer, pharmacy is the last unit visited before discharge. It is likely to influence patient satisfaction and reflect the quality of hospital's service. However, at certain hospitals, the waiting time is long. Resources need to be deployed strategically to reduce queue time. Objective: This research aims to arrange the number of staff (pharmacists and workers) in each station in the pharmacy outpatient service to minimise the queue time.Methods: A discrete simulation method is used to observe the waiting time spent at the pharmacy. The simulation run is valid and effective to test the scenario. Results: It is recommended to add more personnel for the non-compounding medicine and packaging to reduce the waiting time by 22.41%Conclusion: By adding personnel to non-compounding and packaging stations, the system performance could be improved. Cost-effectiveness analysis should be done to corroborate the finding. Keywords:Discrete Event Simulation, Hospital, Outpatient Service, Pharmacy Unit, System Analysis

Warehouse Performance Improvement During Big Event using Discrete Event Simulation approach : A Case Study of Food and Beverages Company in Shah Alam

“…As a result, the use of this modelling simulation is common in a variety of organisations such as health organisations, logistics organisations, manufacturing organisations, and so on. For example, a successfully demonstrated Discrete Event Simulation (DES) is the best modelling simulation approach for solving problems in waiting time for patients (Amelia et al, 2021). As a result, using the same approach in this study, modelling simulation such as DES can have a significant impact on improving the performance of the company's case.…”

Service Time Improvement Proposition For Insurance Customer Operation At Bpjs Kesehatan West Bandung Office: A Discrete Event Simulation Approach

“…An example of discrete is the number of customers in the bank changes only when the customer arrives. In a continuous system, water flows in the lake will increase during rain [16][17][18][19][20][21][22][23][24].…”

Linking Predictive and Prescriptive Analytics of Elderly and Frail Patient Hospital Services

“…Finally, with respect to methodology, we provide a different perspective on how this could be modeled. Previous bed and staffing capacity models have focused on neural networks (Kutafina et al 2019), queuing theoretical approaches (Ghayoomia et al 2022, de Véricourt and Jennings 2011, Yankovic and Green 2011, simulation (Amelia et al 2021, Lu et al 2021. Our methodology builds on traditionally used deterministic methods in healthcare by demonstrating the benefits of using stochastic models by calculating the VSS.…”