Abstract

Maximising the patient flows throughout the emergency care patient pathway is one of the most important objectives in the healthcare system. The emergency department (ED) is the critical point of this pathway in most hospitals, as the potential delays reduce the number of patients seen in the recommended time. One of the key delays in the ED is the waiting time of a patient prior to treatment, which can be reduced by optimising the patient treatment schedules with priorities. In this paper, a novel blocking patient flow (BPF) algorithm is developed and tested using the real data from a hospital in Brisbane, Australia. Initially, a simulation model of real-life ED operations is developed by characterising patient interarrival and treatment times according to different disease categories. Subsequently, a BPF heuristic algorithm is designed and benchmarked via computational experiments using two dominance rules: first come first served (FCFS) and shortest processing time (SPT). The computational results show that the proposed approach leads to a reduction of the total waiting time by more than 8 % in comparison to the current hospital practice, which implies that more patients will be served in a specified time window.

Highlights

  • The emergency department (ED) plays a vital role in the community as it provides appropriate and timely care 24/7 for the public

  • A simulation approach is developed to deal with the uncertainties by defining stochastic variables, such as patient interarrival times and treatment times in the ED system

  • Based on the real-world data collected from the Royal Brisbane and Women’s Hospital (RBWH), extensive computational experiments show that the proposed approach results in an average improvement in the total waiting time performance of 12.02 % by using BPFATT, 9.34 % by using BPFMTT, and 8.48 % by using SPTMTT

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Summary

Introduction

Allihaibi et al (2017) proposed a new ED optimisation model using a stochastic mathematical programming approach under limited budget and resource capacity to optimise the total patient waiting time.

ED Simulation Model
Case study using real hospital data
Findings
Conclusion

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