Robust capacity planning for accident and emergency services

Accident and emergency departments (A&E) are the first place of contact for urgent and complex patients. These departments are subject to uncertainties due to the unplanned patient arrivals. After arrival to an A&E, patients are categorized by a triage nurse based on the urgency. The performance of an A&E is measured based on the number of patients waiting for more than a certain time to be treated. Due to the uncertainties affecting the patient flow, finding the optimum staff capacities while ensuring the performance targets is a complex problem. This paper proposes a robust-optimization based approximation for the patient waiting times in an A&E. We also develop a simulation optimization heuristic to solve this capacity planning problem. The performance of the approximation approach is then compared with that of the simulation optimization heuristic. Finally, the impact of model parameters on the performances of two approaches is investigated. The experiments show that the proposed approximation results in good enough solutions

Dynamic Capacity Planning of Hospital Resources under COVID-19 Uncertainty using Approximate Dynamic Programming

COVID-19 pandemic has resulted in an inflow of patients into the hospitals and overcrowding of healthcare resources. Healthcare managers increased the capacities reactively by utilizing expensive but quick methods. Instead of this reactive capacity expansion approach, we propose a proactive approach considering different realizations of demand uncertainties in the future due to COVID-19. For this purpose, a stochastic and dynamic model is developed to find the right amount of capacity increase in the most critical hospital resources. Due to the problem size, the model is solved with Approximate Dynamic Programming. Based on the data collected in a large tertiary hospital in Turkey, the experiments show that ADP performs better than a benchmark myopic heuristic. Finally, sensitivity analysis is performed to explore the impact of different epidemic dynamics and cost parameters on the results.</p

Dynamic and flexible staff deployment in accident and emergency departments using simulation-based optimization

Accident and emergency departments experience overcrowding due to staff shortages as well as to variations in patient arrivals and the time required to treat them. Several policies have been developed by hospitals to ensure that patients are not put at clinical risk during overcrowding. These policies suggest flexing nurses from different duties to the overcrowded section. However, the policies do not indicate the details of when exactly the flexing should be activated. We develop a mathematical model to find the optimum levels of triage and treatment queue lengths after which flexing should be activated. The performance indicators of the department are the waiting time targets and the disturbance due to nurse flexing. Because of the lack of closed-form formulations, we propose simulation optimization to solve the problem. By analyzing the model structure, we develop an efficient search procedure of the discrete solution space. We show the application of the proposed method using the data of a large hospital in the UK under different parameter settings. The results show that hospital management should focus on increasing the number of treatment nurses rather than flexing the nurses, and the queue of the service stream that requires tighter staffing should be controlled by an upper limit