测量初级保健团队组成对患者激活的影响,利用电子健康记录的大数据分析
Kristen K Will1, Yue Liang2, Chih-Lin Chi2
1Arizona State University, Phoenix, AZ.
Journal of patient-centered research and reviews
|April 10, 2024
概括
多元化,多学科的医疗保健团队 (4+成员) 与更高的患者激活率有关,这是患者参与度的衡量标准. 这项研究使用电子健康记录和机器学习来分析团队组成和患者结果,推进团队科学.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 团队科学 团队科学
- 医疗信息学 医疗信息学
背景情况:
- 基于团队的护理对于高价值,以患者为中心的护理和实现四重目标至关重要.
- 电子健康记录 (EHR) 和机器学习为研究团队动态和患者结果提供了新的途径,克服了数据访问障碍.
- 患者激活是患者参与的关键驱动力,受医疗保健团队组成的影响.
研究的目的:
- 探索医疗保健团队组成和患者激活之间的关系.
- 利用大型电子健康记录 (EHR) 数据集和先进的分析来研究这种关系.
- 将传统的统计方法与机器学习方法进行比较,以分析团队的影响.
主要方法:
- 分析了来自城市卫生系统的大型EHR数据集 (n=316,542).
- 使用共识定义来定义团队,并通过患者激活量 (PAM) 来测量患者激活率.
- 使用多级回归和机器学习 (多项逻辑回归,概括重叠权重) 来评估团队组成对PAM分数的影响.
主要成果:
- 在样本中确定了17个2至5名成员的团队类型 (n=12,448).
- 在控制混变量后,拥有4名或更多成员的多元化,多学科的团队显示了患者激活得分的改善.
- 机器学习分析证实了更大,多学科的团队与更高的患者激活之间的积极关联.
结论:
- 这项研究是第一个使用EHR数据和大数据分析检查团队组成和患者激活的研究.
- 研究结果表明,更大,更多样化的医疗保健团队可以提高患者的激活和参与度.
- 使用EHR数据和机器学习对推进团队科学和理解以患者为中心的护理具有重大前景.
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