ABSTRACT
BACKGROUND/PURPOSE: This study addresses the delicate balance between healthcare personnel burnout and medical accessibility in the context of endovascular thrombectomy (EVT) services in urban areas. We aimed to determine the minimum number of hospitals providing EVT on rotation each day without compromising patient access. METHODS: Employing an optimization model, we developed shift schedules based on patient coverage rates and volumes during the pre-pandemic (2016-2018) and pandemic (2019-2021) periods. Starting with a minimum of two hospitals on duty per day, we gradually increased to a maximum of eight. Patient coverage rates, defined as the proportion of patients meeting bypass criteria and transported to rotating hospitals capable of EVT, were the primary outcomes. Sensitivity analyses explored the impact of varying patient transport intervals and accumulating patients over multiple years. RESULTS: Results from 7024 patient records revealed patient coverage rates of 92.5% (standard deviation [SD] 2.8%) during the pre-pandemic and 91.4% (SD 2.8%) during the pandemic, with at least two rotating hospitals daily. No significant differences were observed between schedules based on the highest patient volume and coverage rate months. A patient coverage rate of 98.99% was achieved with four rotating hospitals per day during the pre-pandemic period, with limited improvement beyond this threshold. Changing patient transport intervals and accumulating patients over six years (p = 0.83) had no significant impact on coverage rates. CONCLUSION: Our optimization model supports reducing the number of daily rotating hospitals by half while preserving a balance between patient accessibility and alleviating strain on medical teams.
ABSTRACT
BACKGROUND: Late cancellations of physical examination has severe impact on the operations of a physical examination center since it is often too late to fill vacancy. A booking control policy that considers overbooking is then one natural solution. Unlike appointment scheduling problems for clinics and hospitals, in which treating a patient mostly requires only one type of resource, a physical examination set typically requires multiple types of resources. Traditional methods that do not consider set-resource relationship thus may be inapplicable. METHODS: We formulate a stochastic mathematical programming model that maximizes the expected net reward, which is the examination revenue minus overage cost. A complete search algorithm and a greedy search algorithm are designed to search for optimal booking limits for all examination sets. To estimate the late cancellation probability for each individual consumer, we apply logistic regression to identify significant factors affecting the probability. After clustering is used to estimate individual probabilities, Monte Carlo simulation is conducted to generate probability distributions for the number of consumers without late cancellations. A discrete-event simulation is performance to evaluate the effectiveness of our proposed solution. RESULTS: We collaborate with a leading physical examination center to collect real data to evaluate our proposed overbooking policies. We show that the proposed overbooking policy may significantly increase the expected net reward. Our simulation results also help us understand the impact of overbooking on the expected number of customers and expected overage. A sensitivity analysis is conducted to demonstrate that the benefit of overbooking is insensitive to the accuracy of cost estimation. A Pareto efficiency analysis gives practitioners suggestions regarding policy determination considering multiple performance indications. CONCLUSIONS: Our proposed overbooking policies may greatly enhance the overall performance of a physical examination center.
