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Queuing Network Model and Visualization for the Patient Flow in the Obstetric Unit of the University of Tsukuba Hospital

机译:筑波大学附属医院产科排队的排队网络模型及可视化

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A complete data set of every movement of all the inpatients from room to room covering two years was provided us by the Medical Information Department of the University of Tsukuba Hospital in Japan. By focusing on the obstetric patients, who are assumed to be hospitalized rather at random times, we have analyzed the patient flow using our original visualization software. Upon admission, each obstetric patient is assigned to a bed in one of the two wards, one for high-risk delivery and the other for normal delivery, and then she may be transferred between the two wards before discharge. We confirm Little's law of queuing theory for the patient flow in each ward. Then we propose a network model of M/G/"V and M/M/m queues to represent the flow of these patients, which is used to predict the probability distribution for the number of patients staying in each ward at the nightly census time from the observed data of patient admission rate and the histogram of the length-of-stay (LOS) in that ward. Although our model is a very rough and simplistic approximation of the real patient flow, the predicted probability distribution is shown to be in good agreement with the observed one. Our method can be used for planning the capacity of obstetric units when the patient demand is predicted.
机译:日本筑波大学医院医学信息部为我们提供了所有住院病人从一个房间到另一个房间的每一个运动的完整数据集,覆盖了两年。通过关注假定是随机住院的产科患者,我们使用原始的可视化软件分析了患者的流向。入院时,每位产科患者被分配到两个病房之一的病床上,一个病床用于高危分娩,另一个病床用于正常分娩,然后她可以在出院前在两个病房之间转移。我们确定了每个病房中患者流动的排队理论的定律。然后,我们提出了一个M / G /“ V和M / M / m队列的网络模型来表示这些患者的流量,该模型用于预测夜间普查时每个病房中的患者数量的概率分布从患者入院率的观察数据和该病房的住院时间直方图来看,虽然我们的模型是真实患者流量的非常粗略和简单的近似,但预测的概率分布显示为与被观察者的一致性很好,当预测患者需求时,我们的方法可用于计划产科容量。

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