Queueing theory techniques and its real applications to health care systems – Outpatient visits

ABSTRACT

Many organizations such as banks, airlines, health care systems, telecommunications companies and security departments routinely use queuing theory models to help determine capacity levels needed to experienced demands in a more efficient way. Although queuing models have been used in hospitals and other health care systems, its applications in this field has not been widely and extensively utilized. Given the perverseness of delays under health care and due to the fact that many facilities are trying to meet the increasing demands with tightly constrained resources, then queuing theory models can be very useful in identifying other opportunities for service improvement. In this article, we build a foundation into the investigation of queuing phenomenon through the review of some applicability of such techniques. Our emphasis is on intuitive understanding of queuing modelling and solution techniques that are useful in applications. We conclude this report by applying its insights and findings to real time data collected from one of the main health facilities in Botswana.

KEYWORDS:

• Queueing theory
• capacity
• health care systems
• health facilities
• resources and services

Acknowledgments

The authors would like to thank their colleagues and peer reviewers for their valuable inputs, comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Notes on contributors

Peter O. Peter

Peter O. Peter, BA (Statistics), MA (Statistics) PhD Student: Peter O. Peter is currently a Statistics PhD Student under School of Mathematical Sciences at Shanghai Jiao Tong University. His main Area of Research is on Data Science with more focus relating to High Dimension Statistical Inference, Data Mining and Machine Learning. Their current Project in on Robust Predictors Ranking Methods for Variable and Model Selection via Extended Bayesian Information Criterion under Ultra High-dimensional Setting (p > n).

R. Sivasamy

R. Sivasamy, PhD is a Professor of Statistics and has taught both undergraduate and graduate statistics courses at the University of Botswana. His main area of research specialization is on stochastic processes and Markov chains. He has published extensively within the field of Quality Control, Clinical trials, Queuing problems and Inventory Management.