Authors (including presenting author) :
Chu WSW(1)(2), Chong CH(1)(2)
Affiliation :
(1)Pharmacy Department, (2)United Christian Hospital.
Introduction :
Discrete-Event Simulation is a computer-based modeling methodology to simulate the behaviour and performance of a real-life process. Computer simulation is useful and could allow us to conduct “what if” by changing various process and to predict their outcomes. The research question is “what is the best discrete event simulation software for pharmacy?”
Objectives :
Inefficiency is undesirable in any sector. For pharmacists facing patients and intense work pressures, advanced technology could improve both productivity and accuracy. This study aims to compare currently available Discrete-Event Simulation (DES) software and to describe the use of the prototype DES in pharmacy.
Methodology :
We identified at least eighteen Commercial-Off-The-Shelf (COTS) and ten Open-Source software (OSS). Those suitable for the healthcare setting were short-listed. To further create a ranking of the selected software, we settled on the following five categories: Functionalities, Cost, Ease of Use, Flexibility, and Development. We then described how to create a prototyping DES using the M/M/1 queue rule. The M/M/1 queuing model is a queuing model where the arrivals follow a Poisson process, service times are exponentially distributed and there is one server. Poisson distribution is a discrete probability distribution that expresses the probability of a given number of events occurring in a fixed interval of time or space if these events occur with a known constant mean rate and independently of the time since the last event.
Result & Outcome :
The results showed that open-Source software type is preferred due to the cost, licensing, copyright and ownership. Free Licensed simulation software written entirely in Python is favoured. We made the computer simulation software using Pythion and followed the single M/M/1 queue to obtain the average waiting time. We then used actual data from one of our General Outpatient Clinic Pharmacies to rectify the program and found that the results were comparable. To conclude, computer-based Discrete Event Simulation is useful to provide vital insights when managing the pharmacy. Further study could be done by creating a multi-server queue (M/M/c queue, while‘c’ could be a numerical number from one to infinity) simulation modeling to mimic and analyse the characteristic of a queuing system at the hospital pharmacy.
