管理和改善患者体验的高级情绪分析:申请北安普敦郡全科医生 (GP) 分类
Aavash Raj Pandey1, Mahdi Seify1, Udoka Okonta1
1Department of Business Systems & Operations, University of Northampton, Northampton NN1 5 PH, UK.
概括
这项研究引入了一种新方法,用于分析来自国家卫生服务 (NHS) 审查的患者反,对全科医生进行分类,以增强医疗保健体验.
科学领域:
- 医疗保健管理的管理
- 在医疗保健中的数据科学.
- 患者体验研究 研究研究
背景情况:
- 患者反对于改善医疗保健质量至关重要.
- 分析患者反的现有方法可能耗时且缺乏深度.
- 了解患者的情绪是提高医疗保健服务的关键.
研究的目的:
- 提出一种新的分析方法,以改善医疗保健机构的患者体验.
- 开发一个工具,通过分类和管理建议,促进及时决策.
- 展示了用于高级患者反分析的可访问工具的集成.
主要方法:
- 使用机器人对国家卫生服务 (NHS) 评论进行网络抓取和情绪分析.
- 从178个全科医生 (GP) 评论中提取关键字在英国北安普敦郡.
- 使用怀卡托知识分析环境 (WEKA) 和Python将全科医生分为性能类别 (金,银,铜),并使用微软Excel进行分析.
主要成果:
- 从178个患者评论中分析了4764个关键词.
- 确定患者反中的关键趋势和模式.
- 基于患者的情绪和反,成功对全科医生进行分类.
结论:
- 开发的分析方法通过利用易于获得的网络数据,为了解患者体验提供了一种新的方法.
- 这种方法补充了现有的患者反分析,提供了更深入的见解.
- 整合可访问工具使得医疗保健服务改进的分析水平更高.
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