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A multivariate time series approach to forecasting daily attendances at hospital emergency department

机译：预测医院急诊部门日常出勤的多元时间序列方法

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Efficient management of patient demands in emergency departments (EDs) has recently received increasing attention by most healthcare administrations. Forecasting ED demands greatly helps ED's managers to make suitable decisions by optimally allocating the available limited resources to efficiently handle patient attendances. Furthermore, it permits pre-emptive action(s) to mitigate and/or prevent overcrowding situations and to enhance the quality of care. In this work, we present a statistical approach based on a vector autoregressive moving average (VARMA) model for a short term forecasting of daily attendances at an ED. The VARMA model has been validated using an experimental data from the paediatric emergency department (PED) at Lille regional hospital centre, France. The results obtained indicate the effectiveness of the proposed approach in forecasting patient demands.
机译：最近，大多数医疗保健管理机构都越来越重视对急诊科（EDs）中患者需求的有效管理。预测急诊室需求将极大地帮助急诊室经理通过最佳分配可用的有限资源来有效地处理患者出勤情况，从而做出适当的决定。此外，它允许采取先发制人的行动来减轻和/或防止人满为患的情况并提高护理质量。在这项工作中，我们提出了一种基于矢量自回归移动平均（VARMA）模型的统计方法，用于对ED的每日出勤率进行短期预测。 VARMA模型已使用来自法国里尔地区医院中心的儿科急诊科（PED）的实验数据进行了验证。获得的结果表明了该方法在预测患者需求方面的有效性。

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