论文标题
在重负荷下，静态与累积的医疗排队优先级
Static vs accumulating priorities in healthcare queues under heavy loads
论文作者
论文摘要
在由Covid-19造成的前所未有的时期，世界各地的医疗保健系统都受到了容量甚至超越容量的限制。由于患者人数的季节性峰值，某些医疗保健系统经常经历类似的事件。我们将其建模为流量繁忙的排队系统（到达率从下面接近服务率）或过载中（到达率超过服务率的情况下）。在这两种情况下，我们都假设客户（患者）可能具有不同的优先事项，我们考虑了两个流行的服务学科：静态优先级和积累优先级。已经表明，只要系统稳定，后者允许及时看到所有班级的患者。但是，我们证明，如果在繁重的交通或超负荷制度中使用积累优先级，那么所有患者（包括优先级最高的患者）都会经历很长的等待时间。另一方面，如果应用静态优先级，则可以确保即使在超载系统中也可以及时看到最高优先级的患者。
Amid unprecedented times caused by COVID-19, healthcare systems all over the world are strained to the limits of, or even beyond, capacity. A similar event is experienced by some healthcare systems regularly, due to for instance seasonal spikes in the number of patients. We model this as a queueing system in heavy traffic (where the arrival rate is approaching the service rate from below) or in overload (where the arrival rate exceeds the service rate). In both cases we assume that customers (patients) may have different priorities and we consider two popular service disciplines: static priorities and accumulating priorities. It has been shown that the latter allows for patients of all classes to be seen in a timely manner as long as the system is stable. We demonstrate however that if accumulating priorities are used in the heavy traffic or overload regime, then all patients, including those with the highest priority, will experience very long waiting times. If on the other hand static priorities are applied, then one can ensure that the highest-priority patients will be seen in a timely manner even in overloaded systems.
