探索影响在线医疗保健患者满意度的因素:基于同情理论的机器学习方法
Junbai Chen1, Guoping Wu1, Tong Zhang1
1Beijing University of Chinese Medicine, Beijing, China.
Digital health
|January 1, 2025
概括
医生同情心显著提高了在线医疗保健中的患者满意度. 关键因素包括了解患者的观点和感知能力,提高整体服务质量.
科学领域:
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
- 患者体验研究 研究研究
背景情况:
- 医生与患者之间的同理心对于患者在医疗咨询中的满意度至关重要.
- 在线医疗保健服务为同情提供了独特的挑战和机会.
- 共感理论为理解患者与提供者之间的互动提供了一个框架.
研究的目的:
- 探索影响在线医疗保健服务患者满意度的因素.
- 调查医生-病人同情对满意度的具体影响.
- 用数据驱动的方法确定患者满意度的关键决定因素.
主要方法:
- 使用自然语言处理 (NLP) 和机器学习 (ML) 算法.
- 从患者与医生的对话中提取了七个变量,这些变量基于杰斐逊医生同情度量.
- 构建并优化了一个分类模型 (LightGBM) 来识别影响因素.
主要成果:
- 分析了来自5447次在线咨询的7586个数据点.
- 在LightGBM模型中获得了0.78的F1得分和0.81的AUC.
- 确定了"站在患者的位置"和"采取视角"作为影响满意度的关键同理心维度.
结论:
- 共感理论对于在线医疗保健中加强医生患者关系至关重要.
- 医生的认知能力和同情能力是患者满意度的关键.
- 结果表明,通过增强的同情心,可以改善在线医疗服务质量的策略.
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