使用INGARCH模型预测急救部门的到来
Juan C Reboredo1,2, Jose Ramon Barba-Queiruga3, Javier Ojea-Ferreiro4
1Department of Economics, University of Santiago (USC), Santiago de Compostela, Spain.
预测急诊室患者的到来是非常重要的. 整数价值的通用自回归条件异种类型 (INGARCH) 模型提高了抵达预测,有助于人员分配和激增管理.
科学领域:
- 医疗保健 运营 研究 研究 研究
- 生物统计学 生物统计学
- 时间序列分析时间序列分析
背景情况:
- 准确预测急诊室 (ED) 患者的到来对于有效的医院管理至关重要.
- 预测患者流动对于资源分配和减轻患者激增的影响至关重要.
研究的目的:
- 评估历史患者到达数据对预测每日ED到达的有用性.
- 评估过去的平均值和观察是否提高了预测的准确性.
主要方法:
- 使用一个整数值的通用自回归条件异类 (INGARCH) 模型.
- 整合了过去的入境数据和分析了入境波动动态.
- 检查了条件分配适合性和预测性能.
主要成果:
- INGARCH模型显示,在样本和样本之外的预测准确度有所提高.
- 预测的改进在抵达分布的下方和上方量子位上尤其显著.
- 该模型有效地捕捉到达波动的动态.
结论:
- INGARCH 建模为短期,战术性应急部门规划提供了有价值的工具.
- 这种方法有助于优化员工轮流和资源部署,以应对意想不到的患者涌入.
- 通过改进预测,提高紧急部门的运营效率.
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