患者特征和临床医生在决策中涉及患者的程度:聚合数据的二次分析
Sascha M Keij1, Megan E Branda2, Victor M Montori2
1Medical Decision Making, Department of Biomedical Data Sciences, Leiden University Medical Center, The Netherlands.
概括
在实践中,共享决策 (SDM) 是有限的. 患者教育水平是与SDM相关的唯一特征,但SDM工具无论教育程度如何都改善了患者参与.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 以患者为中心的护理
- 临床交流 临床沟通
背景情况:
- 共享决策 (SDM) 对于以患者为中心的护理至关重要,但在临床实践中仍未得到充分利用.
- 假设患者的特征会影响临床医生将患者纳入SDM的程度.
研究的目的:
- 调查各种患者特征之间的关联以及临床医生参与SDM的程度.
- 评估SDM对话工具对不同患者人口统计数据的患者参与的影响.
主要方法:
- 来自10项涉及临床接触的视听记录的研究数据的二次分析.
- 使用观察患者参与决策 (OPTION®12) 规模来量化SDM.
- 多变量分析检查了患者特征 (年龄,性别,教育等) 之间的关联. 和OPTION®12分数. 这是一个很好的选择.
主要成果:
- 分析包括1614名患者;干预组和对照组之间观察到OPTION®12得分的显著差异 (7.7/100分,P < 0.001).
- 教育水平与OPTION®12分数有很小的关联,主要是没有SDM工具 (P = 0.030).
- 一项中风预防试验的次分析表明,年龄和OPTION®12分数之间存在正相关性 (P = 0.033).
结论:
- 大多数患者的特征不会显著影响临床医生参与SDM.
- 临床医生在没有工具的情况下更多地让具有高等教育的患者参与SDM,SDM工具可以缓解这种差异.
- SDM对话工具可以提高公平的患者参与,无论其教育背景如何.
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