使用在线评论来推动以人为中心的护理:一个HCAHPS验证的方法
Joseph G Taylor1, Meghan P Leaver1, Alex Griffiths1
1PEP Health, London, UK.
Journal of patient experience
|July 30, 2025
概括
在线患者评论提供了对医疗保健体验的实时洞察. 机器学习可以分析这些数据,以改善以人为中心的护理,并评估质量,与既定的评级保持一致.
科学领域:
- 医疗保健服务研究 医疗服务研究
- 计算语言学 计算语言学
- 医疗保健中的人工智能
背景情况:
- 以人为中心的护理优先考虑个体患者的需求和协作治疗规划.
- 在线患者评论是一个未充分利用的,丰富的实时患者体验数据来源.
- 量化非结构化在线数据对于系统分析患者反至关重要.
研究的目的:
- 展示在线患者评论对评估以人为中心的护理的有用性.
- 开发和验证用于分析患者审查数据的机器学习模型.
- 评估在线审查数据与已建立的医疗保健质量指标的一致性.
主要方法:
- 一个受监督的机器学习模型在大量手动注释的患者评论数据集上受训.
- 使用自然语言处理 (NLP) 技术来量化非结构化审查数据.
- 模型的性能被评估为预测整体得分的准确性和与特定患者体验领域的相关性.
主要成果:
- 该模型在预测整体患者评审得分时达到99%的准确性.
- 该模型在识别患者体验领域 (如有效治疗和情绪支持) 中显示了93%至99%的准确性.
- 综合的在线患者评论显示了与HCAHPS星级评级的统计学上显著的对齐.
结论:
- 通过机器学习分析在线患者评论,为增强以人为中心的护理提供了宝贵的工具.
- 这种方法使医疗保健系统的客观比较和质量改善干预措施的评估成为可能.
- 这些发现支持将在线患者反纳入医疗保健质量评估框架.
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