Nowadays, the rapidly increasing healthcare cost has become a serious problem because of inefficient usage of medical resources. The Emergency Department (ED) plays a vital role in the hospital system and has critical effects on the overall efficiency in a hospital. The ED deals with patient's arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, decisions to discharge or admit access to inpatient beds and physicians. These activities generally occur in a sequential manner and the delayed activities of the patient flow can cause bottlenecking and reduce the service level. Optimising the service of the ED is challenging because the arrival times of patients are dynamic and their expected treatment times are volatile. This paper develops a new ED optimisation model using stochastic mathematical programming approach under limited budget and resource capacity. The objectives of the proposed model are for increasing the system efficiency, serving more patients in specific time, or providing the same quality of the service with the use of less medical resources. A numerical investigation is presented and demonstrates that high-quality solutions are obtainable for industry-scale applications in a reasonable time. Computational experiments have been conducted using CPLEX and ExtendSim to solve the ED-Stochastic Optimisation Mixed Integer Programming model and ED-Simulation model sequentially. Real data for Royal Brisbane and Women's Hospital (RBWH) is used in this paper to validate the proposed solution approach.
One of the most critical objectives in the healthcare system is maximising patient flows in the emergency care patient pathway. Patient emergency flow analysis indicates that the timetabling of a patient's movement from one activity to another through the Emergency Department (ED) is critical for treating patients. The ED deals with the patient's arrival, triage, physician assessment, imaging and laboratory studies, treatment planning, nursing procedures, and decisions to admit or discharge the patient. Any delayed activities in patient flow reduce the service level of healthcare. To address these challenges, this paper develops a stochastic ED Simulation-Optimisation approach by considering stochastic variables, such as patient interarrival times and treatment times, using statistical distributions. This type of distribution depends on two main elements: day shifts and patient categories. A hybrid evolutionary algorithm is integrated with the simulation to find a satisfactory solution for this stochastic optimisation problem in real time. Computational experiments show that the proposed approach can serve more patients in specific time windows or provide the same quality of the service with the use of fewer medical resources.
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