开发一个评分系统,从事件报告中的语义特征量化错误
Haruhiro Uematsu1, Masakazu Uemura2, Masaru Kurihara2
1Department of Quality and Patient Safety, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan hiro_uematsu@hotmail.com.
这项研究开发了一个使用自然语言处理在事件报告上的错误评分系统. 该系统准确地识别患者安全风险,帮助组织学习和改善护理.
科学领域:
- 医疗保健信息学 医疗保健信息学
- 患者安全研究 患者安全研究
- 自然语言处理应用程序
背景情况:
- 事件报告系统对于识别风险和促进组织学习至关重要.
- 事件报告中的自由文本描述包含有关促成因素的宝贵数据.
- 现有的方法可能无法充分利用自由文本事件叙述中的丰富信息.
研究的目的:
- 开发和验证事件报告的自动错误评分系统.
- 用一种包含自然语言处理的新型决策模型提取关于错误因子的信息.
- 量化事件严重程度,并确定改善患者安全的贡献因素.
主要方法:
- 追溯分析了来自名古屋大学医院的114,000多份自由文本事件报告 (2012-2022年).
- 应用形态分析和决策模型用于术语细分和错误评分.
- 使用准确性,回忆,精度和F-score指标对专家分类进行"报告错误评分"的验证.
主要成果:
- 在专家识别的与错误有关的事件和其他事件之间观察到错误得分的显著差异 (p<0.001).
- 拟议的"报告错误得分"实现了高性能指标:准确性 (0.8),回忆 (0.82),精度 (0.85),和F-score (0.84).
- 团队错误得分与所有部门的专家评级有着强烈的积极关联 (相关性=0.66,p<0.001).
结论:
- 开发的错误评分系统有效地利用综合事件报告数据来识别患者安全风险.
- 这种自动化方法为增强患者安全计划提供了有价值的见解.
- 该系统通过提供与事件相关的错误的定量度量来支持组织学习.
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