A new medical staff allocation via simulation optimisation for an emergency department in Hong Kong

Abstract

Whether triage targets can be achieved has been an imperative assessment of service qualities for an emergency department in healthcare management. In this research, we focus on triage targets and try to fully meet the target of fast emergency response for critical patients subject to triage requirements for other category patients by optimising the medical staff allocation in the emergency department. Main challenges stem from multiple stochastic constraints and the time-consuming simulation. To solve the stochastically constrained discrete optimisation via simulation problem, we develop a discrete-event simulation model and propose a simulated-annealing-based algorithm called ConSA that adopts a special searching mechanism and an efficient simulation budget allocation rule to find a high-quality configuration of medical staff. A case study based on the data from a public hospital in Hong Kong is carried out. Numerical experiments demonstrate that our algorithm leads to a 38.28% improvement in the main performance compared to the current staff allocation and dominates other algorithms in terms of computational efficiency and output accuracy. It indicates that our method is a good decision tool for hospital managers.

Keywords:

• healthcare management
• medical resource allocation
• discrete optimisation via simulation
• stochastic constraint
• simulated annealing

Acknowledgments

We thank Siyang Gao, the assistant professor with the Department of Systems Engineering and Engineering Management in City University of Hong Kong, for giving us support from the methodological perspective. We also thank the anonymous reviewers and the Department Editor for their constructive comments and suggestions which have greatly improved the exposition of this paper.

Disclosure statement

No potential conflict of interest was reported by the authors.

ORCID

Wenjie Chen http://orcid.org/0000-0003-4949-8949

Additional information

Funding

This work was supported by National Natural Science Foundation of China (NSFC) [grant number 71701132] and Research Grants Council (RGC) Theme-Based Research Scheme [grant number T32-102/14-N].