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Queueing network model for obstetric patient flow in a hospital

机译:产科患者流动的排队网络模型

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A queueing network is used to model the flow of patients in a hospital using the observed admission rate of patients and the histogram for the length of stay for patients in each ward. A complete log of orders for every movement of all patients from room to room covering two years was provided to us by the Medical Information Department of the University of Tsukuba Hospital in Japan. We focused on obstetric patients, who are generally hospitalized at random times throughout the year, and we analyzed the patient flow probabilistically. On admission, each obstetric patient is assigned to a bed in one of the two wards: one for normal delivery and the other for high-risk delivery. Then, the patient may be transferred between the two wards before discharge. We confirm Little's law of queueing theory for the patient flow in each ward. Next, we propose a new network model of M/G/ a 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. Although our model is a very rough and simplistic approximation of the real patient flow, the predicted probability distribution shows good agreement with the observed data. The proposed method can be used for capacity planning of hospital wards to predict future patient load in each ward.
机译:排队网络用于利用观察到的患者入院率和每个病房的患者住院时间直方图来模拟医院的患者流量。日本筑波大学医院医疗信息部门为我们提供了一个完整的记录,记录了所有患者从一个房间到另一个房间的每两年的每一个动作。我们关注的是产科患者,他们通常在一年中的随机时间住院,我们对患者流量进行了概率分析。入院时,每位产科患者被分配到两个病房中的一个病床:一个用于正常分娩,另一个用于高危分娩。然后,患者可以在出院前在两个病房之间转移。对于每个病房的病人流量,我们确认了利特尔排队理论定律。接下来,我们提出了一个新的M/G/a和M/M/M队列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的住院患者数量的概率分布。虽然我们的模型是真实患者流量的一个非常粗略和简单的近似,但预测的概率分布与观察数据显示出良好的一致性。该方法可用于医院病房容量规划,以预测每个病房未来的患者负荷。

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