评估患者对医疗决策的偏好 - - 不同方法的比较
Jakub Fusiak1, Andreas Wolkenstein2, Verena S Hoffmann1
1Institute for Medical Information Processing, Biometry, and Epidemiology, Ludwig-Maximilians-Universität München, Munich, Germany.
Frontiers in digital health
|December 1, 2025
概括
评估患者偏好的PAPRIKA方法在清晰度和可用性方面被评为最高. 结构化偏好诱导方法可以增强共享决策 (SDM),当集成到临床工作流程.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 决策科学 决策科学 决策科学
- 以患者为中心的护理
背景情况:
- 患者的偏好对于共享决策 (SDM) 至关重要,尤其是在具有不同风险和结果的治疗方法之间进行选择时.
- 现有的诱导患者偏好的方法在复杂性,可用性和接受性上有所不同.
研究的目的:
- 评估参与者对五种不同的偏好评估方法的接受,努力和偏好.
- 调查健康状况,医疗保健经验和人口统计数据如何影响这些评估.
主要方法:
- 一项横截面的在线调查评估了五种偏好诱导方法:最佳-最差缩放,直接加权,PAPRIKA,时间权衡和标准博.
- 参与者对方法的清晰度和可用性进行了评分;额外的项目评估了算法辅助评估的接受度和问卷清晰度.
- 该调查通过学术和患者倡导名单针对健康人群和患有疾病的人群.
主要成果:
- 帕普里卡方法在清晰度,可用性和偏好表达方面获得了最高评分.
- 较简单的方法对细微的偏好效果较差,而基于实用性的方法对认知要求要求较高.
- 大多数参与者发现至少有一种方法在临床环境中可用,强调医生参与.
结论:
- 交互式PAPRIKA方法有效地平衡了认知需求和表现力,使其成为首选选择.
- 结构化偏好诱导可以改善SDM如果集成到临床工作流程与医疗保健专业人员的支持.
- 需要进一步的研究来评估这些方法在现实世界的决策和多样化的人口.
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