从患者的声音到政策:数据分析揭示了安大略省医院反的模式
Pourya Momtaz1, Mohammad Noaeen1, Konrad Samsel1
1Institute of Health Policy, Management and Evaluation, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.
PLOS digital health
|February 5, 2026
概括
患者满意度审查显示系统性差距,在COVID-19期间负面反减少. 然而,服务于边缘化社区的医院在2020年后的积极评价下降,突出了健康不平等.
科学领域:
- 医疗保健管理的管理
- 自然语言处理自然语言处理.
- 健康 公平 研究 健康 公平 研究
背景情况:
- 患者满意度对于医疗保健质量评估至关重要.
- 在规模上评估患者满意度存在重大挑战.
- 对患者反的大规模分析可以揭示系统性问题.
研究的目的:
- 在医院评论中使用自然语言处理 (NLP) 分析患者满意度趋势.
- 调查COVID-19大流行对患者满意度的影响.
- 检查社会经济因素和流行病如何影响边缘化社区的患者体验.
主要方法:
- 分析了来自安大略省45家医院的122,194份被取消身份的患者评论 (2015年1月至2022年7月).
- 应用NLP和临床命名实体识别 (NER) 来提取医学概念.
- 地理空间分析将情绪与人口普查数据相关联 (低收入,可见少数群体).
- 多变量逻辑回归用于评估正面评价比例随时间变化和社会经济群体的变化.
主要成果:
- 超过80%的医院有不到50%的积极评价,这表明潜在的系统性差距.
- 在COVID-19大流行期间,负面评论减少了.
- 牙科和放射科单位的负面评价比例较高.
- "焦虑"是负面评论中经常出现的问题,特别是在大流行初期.
- 高少数民族/低收入地区的医院最初有更积极的评论,但在2020年后,这种情况发生了逆转.
- 统计模型证实了显著的转变,特别是在低收入服务医院.
结论:
- 大规模的非结构化患者数据可以确定满意度和系统不平等的关键驱动因素.
- COVID-19大流行改变了患者满意度的动态,最初有所改善,但后来在边缘化地区出现逆转.
- 迫切需要采取适应性策略,以解决焦虑等心理健康问题,并减少系统性健康不平等.
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