使用行政和索赔数据预测糖尿病的医疗补助患者的再入院情况
Jaehyeon Yun, Giovanni Filardo, Vishal Ahuja
1Department of Statistics & Data Science, Southern Methodist University, 144 Heroy Hall, 3225 Daniel St, Dallas, TX 75275-0332.
The American journal of managed care
|August 24, 2023
概括
一个新的模型可以识别30天再入院的高风险糖尿病的住院医疗补助患者. 该工具有助于预测再接收风险,以更好地管理医疗保健和资源配置.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 医疗信息学 医疗信息学
- 糖尿病 护理 糖尿病 护理
背景情况:
- 对于糖尿病的医疗补助患者来说,再入院是一个重要的关注点,导致医疗保健成本增加.
- 预测模型可以帮助识别针对性干预的高风险个体.
研究的目的:
- 开发和验证一个预测模型,用于诊断为糖尿病的医疗补助患者的30天再入院.
- 确定与该人口再接收风险相关的关键因素.
主要方法:
- 利用了美国7个州的2016-2019年医疗补助申请数据.
- 用最小绝对收缩的物流回归和选择操作员 (LASSO) 进行变量选择.
- 使用图形评估和接收器操作特征曲线 (AUROC) 下的区域评估模型性能.
主要成果:
- 该研究分析了69640名符合条件的患者中的129170次住院,30天再入院率为22.8%.
- 最终的预测模型包括人口统计数据,过去的诊断,入院细节,出院状态和停留时间.
- 该模型实现了0.761的强大的AUROC,这表明预测能力中等.
结论:
- 开发的模型在识别30天再入院高风险的糖尿病医疗补助患者方面表现出适度的准确性.
- 该模型作为风险分层和标准化医疗保健机构再接收率比较的模板.
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