从录取到释放:利用NLP进行上游初级编码,使用SNOMED CT
Elisa Asensio Blasco1, Xavier Borrat Frigola1,2, Xavier Pastor Duran1,3
1Clinical Informatics Service, Hospital Clínic de Barcelona, Barcelona, Spain.
Journal of medical systems
|May 21, 2025
概括
通过NLP工具实现自动化的SNOMEDCT编码健康问题列表,显著提高了临床编码的准确性和效率. 这增强了数据的重用,用于医疗保健研究和管理.
科学领域:
- 医疗信息学 医疗信息学
- 健康数据管理 管理健康数据
- 临床术语学 临床术语学
背景情况:
- 准确和高效的临床编码对于医疗保健数据的利用至关重要.
- 手动编码过程可能会导致延迟和数据不一致.
- 像SNOMED CT这样的标准化术语对于互操作性至关重要.
研究的目的:
- 描述SNOMEDCT编码健康问题列表在巴塞罗那临床医院的实施情况.
- 使用自然语言处理 (NLP) 工具自动化健康问题编码.
- 提高临床数据的准确性,效率和可重复使用性,用于研究和管理.
主要方法:
- 选择了SNOMED CT作为参考术语.
- 创建了一个本地健康问题目录 (HPC) 子集.
- 在临床工作站中集成了一个NLP工具,用于自动编码.
- 采用了系统架构,用于编码和审查的专用服务器.
主要成果:
- 在2024年4月至10月期间,记录了超过118,000个健康问题.
- 74.2%的健康问题是使用NLP工具实时编码的.
- 编码延迟大大减少,数据仓库得到了丰富.
结论:
- 使用NLP实现SNOMEDCT编码的健康问题列表可以提高编码准确性和临床医生的效率.
- 该系统促进实时研究,并支持数据驱动的医疗保健决策.
- 改善临床理解和基于证据的建议是关键的结果.
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