需求预测为更好的医院容量管理
Gabrielle Josling1, Justin Boyle1, Vahid Riahi1
1Australian e-Health Research Centre, CSIRO, Australia.
Studies in health technology and informatics
|November 14, 2025
概括
准确的医院床需求预测至关重要. 先进的需求预测工具 (ADePT) 在预测住院患者的入院和分离方面表现强,有助于资源配置和能力规划.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 数据科学数据科学数据科学
- 运营研究 运营研究
背景情况:
- 有效的医院床位管理对于患者护理和资源优化至关重要.
- 准确预测患者流量是医疗保健系统的一个持续挑战.
研究的目的:
- 评估统计和机器学习模型来预测每日/每小时的医院需求.
- 为医疗预测引入和评估高级需求预测工具 (ADePT).
主要方法:
- 在ADEPT工具中使用了SARIMAX时间序列模型.
- 将ADePT与使用现实世界三级医疗服务数据的其他五种预测模型进行比较.
- 评估了住院患者入院,分离和急诊室演示的预测.
主要成果:
- ADePT在各种预测时间段内,总体上优于其他住院患者入院和分离的模型.
- 对于急诊室的陈述,没有观察到统计学上显著的准确差异.
- 对于较小的患者子组,包括急诊和选择性入院,ADEPT的准确性很高.
结论:
- ADePT为短期和长期医院需求预测提供了可靠的解决方案.
- 该工具可以显著改善每日病床管理和长期医疗保健能力规划.
- 准确的预测支持有效的资源配置和增强的患者护理服务.
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