使用定性和计算文本分析从自由文本护理经验调查数据中产生可操作的见解:研究协议
Daniela Rohde1, Mona Isazad Mashinchi2, Nina Rizun2,3
1National Care Experience Programme, Health Information and Quality Authority, Dublin, Ireland.
HRB open research
|November 23, 2023
概括
这项研究分析了来自国家调查的患者反,使用文本分析来确定影响护理体验的关键因素. 它的目的是开发一个持续的,标准化的分析工具,以推动医疗和社会护理服务的质量改善.
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
- 数据科学数据科学数据科学
背景情况:
- 国家护理经验计划 (NCEP) 通过开放式调查问题收集有价值的患者定性反.
- 目前对这些丰富数据的分析缺乏统一的方法,限制了对服务质量的影响.
- 需要系统地分析患者的体验,以确定积极和消极护理结果的驱动因素.
研究的目的:
- 分析来自NCEP调查的定性反应,以确定患者体验的关键因素.
- 确定医疗保健政策,监管和监测需要改进的领域.
- 开发一种可访问的工具,用于对定性数据的持续分析,为质量改进提供可操作的见解.
主要方法:
- 利用计算文本分析和深度学习技术,分析了来自全国住院患者经验调查和全国产妇经验调查的93,000多条评论.
- 采用基于服务管理文献和NCEP数据的全面分析框架进行自动化分析.
- 采用基于场景的设计,有效地向决策者展示洞察力.
主要成果:
- 确定关键的护理活动,资源和与积极和消极的患者体验相关的上下文因素.
- 确定改善医疗保健服务提供和政策的具体领域.
- 建立一个工具的基础,以简化对患者定性反的分析.
结论:
- 这项研究最大限度地发挥了护理经验调查的定性数据的潜力,用于质量改进计划.
- 开发的工具将使患者反的分析更加有效和标准化.
- 加快将患者体验数据转化为针对性的医疗和社会护理质量改进.
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