加强紧急部门管理:以数据驱动的方法来检测和预测激增持续性的方法
Kang Heng Lim1,2, Francis Ngoc Hoang Long Nguyen1, Ronald Wen Li Cheong1
1Health Services Research Centre, Singapore Health Services Pte Ltd., Singapore 169856, Singapore.
Healthcare (Basel, Switzerland)
|September 14, 2024
概括
早期预警系统使用先进的模型预测急诊室 (ED) 出勤量激增. 这种主动的方法提高了医疗保健规划和医院在健康危机期间的准备.
科学领域:
- 医疗保健管理的管理
- 公共卫生分析 公共卫生分析
- 时间序列预测时间序列预测
背景情况:
- 有效的医疗保健规划和资源分配取决于预测急诊室 (ED) 患者出勤率.
- 无法预见的健康危机需要强大的系统,以便在ED中及时进行操作调整.
研究的目的:
- 开发和验证一个早期预警系统,以检测新出现的趋势,并预测ED出席率的激增.
- 通过积极的行动计划,提高紧急部门的准备和灵活性.
主要方法:
- 利用了从2010年1月到2022年12月的13年历史ED出勤数据 (1,700,887个数据点).
- 开发并验证了三种模型:带有外源因子的季节性自回归集成移动平均线 (SARIMAX),用于激增预测的指数加权移动平均线 (EWMA) 和趋势持久性模型.
- 采用了EWMA控制图表用于漂移检测和机器学习模型,用于预测趋势持久性,在核回归ED出席曲线的斜率上进行训练.
主要成果:
- EWMA的控制图表成功地确定了新加坡的重大COVID-19事件.
- 激增预测模型在COVID-19大流行期间 (2020年1月至2022年12月) 提供了ED出勤趋势变化的早期信号.
- 趋势持久性模型估计了新趋势持久性,平均绝对误差为7.54天 (95% CI:6.77-8.79).
结论:
- 该研究引入了一种针对紧急医疗管理的积极激增检测框架.
- 这一框架对于改善紧急部门在卫生危机期间的准备和响应能力至关重要.
- 开发的系统提供了及时的警报,以便在ED中进行主动的操作规划.
关键词:
埃沃玛 (EWMA) 是一家在欧洲的公司.萨里马克斯 (SARIMAX) 是一种药物.控制图表 控制图表漂移检测 漂移检测 漂移检测紧急情况部门拥挤过度拥挤机器学习是机器学习.时间序列时间序列更多相关视频
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