护理过程和总健康成本的变化:使用机器学习的分析.
Maria Consuelo Company-Sancho1,2, Victor M González-Chordá3,4,5, Maria Isabel Orts-Cortés3,5,6
1Health Promotion Service, Canary Islands Health Service, Directorate General for Public Health, Las Palmas de Gran Canaria, 35003, Spain. chelocomp@gmail.com.
BMC nursing
|July 2, 2025
概括
护理过程数据显著影响医疗保健成本预测,解释了超过21%的成本变化. 纳入这些信息可以改善资源配置和医疗保健规划.
科学领域:
- 医疗保健经济学 医疗保健经济学
- 护理信息学 在护理信息学.
- 医疗服务研究 医疗服务研究
背景情况:
- 西班牙医疗保健系统目前没有将护理过程数据纳入支出计算.
- 这种遗漏可能导致资源分配效率低下和医疗保健管理不足.
研究的目的:
- 确定护理过程信息,包括功能模式和从临床病史中获得的NANDA-NIC-NOC分类,是否会影响总医疗保健成本的预测.
- 评估护理数据在医疗保健支出模型中的预测价值.
主要方法:
- 一项分析性,回顾性观察性研究,涉及15岁以上的1,691,075名个人.
- 解释变量包括年龄,性别和护理过程数据;总医疗费用是结果.
- 两变量分析,多重回归和机器学习模型 (神经网络,XGBoost) 用于多变量分析.
主要成果:
- 58%的人口拥有护理过程数据. 经过评估的护理模式的个人平均成本 (€2304.17) 比没有 (€950.93) 的人高 (2304.17).
- 那些接受护理诊断的人的平均费用比那些没有护理诊断的人 (840欧元) 高 (1666欧元).
- 机器学习模型的确定系数 (R2) 为21.45%,表明具有显著的预测能力.
结论:
- 开发的模型,包括年龄,性别和护理过程信息,解释了总医疗费用变化的21%以上.
- 强调护理对患者成本的影响,可以支持将其纳入管理计划和风险调整模型,以改善医疗保健规划.
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