通过多数据预测进行动态门诊呼吸道鼻感染控制方法的研究
Yuncong Wang1, Wenhui Ma1, Yang Yang1
1Hospital Infection Management Division, Xuanwu Hospital Capital Medical University, Beijing, People's Republic of China.
Risk management and healthcare policy
|April 21, 2025
概括
这项研究开发了一种动态方法来控制门诊患者的波动性呼吸道医院感染. 该方法使用自回归集成移动平均 (ARIMA) 模型和层次感染控制措施,以实现准确,低成本的预防.
科学领域:
- 流行病学 流行病学
- 传染病控制和控制传染病
- 数学建模的数学建模
背景情况:
- 鼻腔感染在医疗保健环境中构成了重大挑战.
- 波动的感染率需要适应性预防和控制策略.
- 目前的方法可能缺乏实时风险评估的动态调整能力.
研究的目的:
- 开发一种动态的预防和控制方法,用于门诊患者的波动性呼吸道病房感染.
- 将预测建模与基于共识的感染控制措施相结合.
- 为实时风险评估和干预提供一个框架.
主要方法:
- 利用自回归集成移动平均 (ARIMA) 模型,利用监测数据预测流行病的高峰发作和持续时间.
- 采用了德尔菲流程,以建立不同流行病阶段对等级感染控制措施的共识.
- 开发了一个风险期判断矩阵,将监控数据与控制措施联系起来.
主要成果:
- 阿里马模型展示了准确的预测能力,平均绝对百分比误差在9.2%至25.6%之间.
- 层次的感染控制措施被分为三个组,有九个关键点.
- 一个风险期判断矩阵成功设计,将监控数据与适当的控制策略联系起来.
结论:
- 为在门诊患者中动态预防和控制呼吸道感染而构建了一个数学模型.
- 该方法利用日常医疗服务监测数据进行有效的感染管理.
- 预计在医疗机构的实施将产生准确且具有成本效益的感染预防和控制结果.
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