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时间序列机器学习模型,以支持紧急部门的运营计划

Tamanna T K Munia¹, Kyle Marshall¹, Kitae Kim¹

¹Geisinger, Danville, PA.

AMIA ... Annual Symposium proceedings. AMIA Symposium

|February 23, 2026

概括

使用先知模型预测急诊室 (ED) 的利用率有助于医院资源规划. 这种以用户为中心的方法改善了员工安排和患者流量管理的日常运营决策.

科学领域:

医疗服务研究医疗服务研究
运营研究运营研究
数据科学数据科学数据科学

背景情况:

有效的急诊部 (ED) 使用率预测对于医院资源管理和人员安排至关重要.
现有的预测方法很少在现实世界的操作环境中实施.
需要以用户为中心的设计方法来弥合预测模型和运营需求之间的差距.

研究的目的:

开发和实施一个准确的ED利用预测模型,适合运营规划.
让护理业务经理参与选择关键指标,模型和预测地平线.
为ED的运营领导者创建一个生产仪表板.

主要方法:

采用以用户为中心的设计方法,涉及多个医院站点的护理运营经理.
使用平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 评估了各种时间序列和机器学习模型.
选择并实施了Prophet模型,这是一个开源预测工具,因为它的性能优越.

主要成果:

根据MAE和MAPE,先知模型在基于MAE和MAPE的多个医院站点展示了最佳性能.
为关键的ED指标生成每日14天的预测,包括到达,入院,保姆需求和ED持有.
该模型的实施和监控设计是为持续的运营使用而建立的.

结论:

一种以用户为中心的方法成功地将高级预测 (Prophet模型) 集成到ED的运营规划中.
准确的,短期的ED利用预测可以显著提高资源分配和人员配置决策.
这种方法提供了一个可扩展的解决方案,以改善综合卫生系统内的ED管理.

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