No abstract

DES helps the healthcare service providers describe the current situation, and simulate the what-if scenarios for future planning.

By understanding the profile of the patients and their utilization patterns in the three regional health systems, our study will help clinicians and decision makers design appropriate integrated care programs for patients with the aim of covering the healthcare needs for the enitre population across the healthcare spectrum in Singapore. Copyright © 2015 John Wiley & Sons, Ltd.

This paper is focused on the factors causing long patient waiting time/clinic overtime in outpatient clinics and how to mitigate them using discrete event simulation. A two-week period of data collection is conducted in an outpatient clinic of a Singapore government hospital. Detailed time study from patient arrival to patient departure is conducted, and the possible factors causing long patient waiting time/clinic overtime are discussed. A discrete simulation model is constructed to illustrate how to improve the clinic performance by mitigating the detected factors. Simulation and implementation results show that significant improvement is achieved if the factors are well addressed.

Patient queues are prevalent in healthcare and wait time is one measure of access to care. We illustrate Queueing Theory-an analytical tool that has provided many insights to service providers when designing new service systems and managing existing ones. This established theory helps us to quantify the appropriate service capacity to meet the patient demand, balancing system utilization and the patient's wait time. It considers four key factors that affect the patient's wait time: average patient demand, average service rate and the variation in both. We illustrate four basic insights that will be useful for managers and doctors who manage healthcare delivery systems, at hospital or department level. Two examples from local hospitals are shown where we have used queueing models to estimate the service capacity and analyze the impact of capacity configurations, while considering the inherent variation in healthcare.