在国际医院数据中对诊断码进行分类的工具的开发和外部验证
Sarah L Malecki1, Anne Loffler2, Daniel Tamming2
1Department of Medicine, University of Toronto, Toronto, ON, Canada.
International journal of medical informatics
|June 8, 2024
概括
一个新的算法增强了国际疾病分类第十版 (ICD-10) 代码的映射到临床分类软件精制 (CCSR) 类别. 该工具显著提高了加拿大和丹麦健康数据的分类准确性,有助于研究和质量测量.
科学领域:
- 医疗信息学 医疗信息学
- 医学分类系统 医学分类系统
- 国际卫生数据标准 国际卫生数据标准
背景情况:
- 完善的临床分类软件 (CCSR) 将国际疾病分类第十版 (ICD-10) 的代码分组为分析.
- 在美国开发的CCSR面临国家特定的ICD-10编码系统的挑战.
研究的目的:
- 开发和验证一个算法,以半自动匹配加拿大ICD-10-CA代码与CCSR类别.
- 通过加拿大和丹麦的住院患者数据来评估算法的性能.
主要方法:
- 开发了一种半自动算法,将加拿大ICD-10-CA代码映射到CCSR类别.
- 利用了7家加拿大医院 (2010-2020年) 的出院诊断和丹麦 (2017-2018年) 的住院患者数据.
- 手动验证结果并使用丹麦数据进行外部验证.
主要成果:
- 直接CCSR分类仅适用于46.6%的加拿大和49.4%的丹麦代码.
- 开发的算法映射了98.5%的加拿大和97.7%的丹麦代码.
- 临床验证显示了高准确度 (加拿大数据为97.1%,丹麦数据为97.0%).
- 许多常见的疾病,如心力衰竭,在没有算法的情况下无法直接绘制.
结论:
- 吉米尼 (GEMINI) CCSR匹配算法显著改善了ICD-10代码对临床上有意义的组进行分类.
- 这种开源算法增强了加拿大,丹麦和可能其他国家的研究和质量测量数据的可用性.
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