链接MD:通过4个链接算法连接医疗和牙科记录
1Center for Artificial Intelligence, Data Science, and Informatics, Department of Oral Health Sciences, Artificial Intelligence, Data Science, and Informatics School of Dentistry, Philadelphia, PA, USA.
Journal of dental research
|November 19, 2025
概括
通过新的算法,现在可以将电子健康和牙科记录联系起来. 这提高了患者数据的完整性,并支持综合医疗保健,弥合了医学和牙科之间的差距.
科学领域:
- 医疗信息学 医疗信息学
- 生物医学数据链接
- 跨学科医疗保健 跨学科的医疗保健
背景情况:
- 电子健康记录 (EHR) 和电子牙科记录 (EDR) 通常是孤立的,阻碍了综合护理和研究.
- 现有的基础设施和互操作性挑战使医疗和牙科领域之间无法无共享数据.
- 这种分离限制了基于实践的证据生成和全面的患者管理的潜力.
研究的目的:
- 开发和验证算法框架,以在非集成系统中链接孤立的EHR和EDR数据.
- 评估不同链接策略在准确连接患者记录方面的表现.
- 通过整合医疗和牙科数据,提高患者人口统计信息的完整性.
主要方法:
- 开发和评估了四个算法框架:直接社会保障号码匹配,未加权相似度得分,加权平均相似度得分和概率分类模型.
- 利用一个大型数据集,包括超过170万个医疗记录和222480个牙科记录从寺大学超过10年的时间.
- 通过对1000个候选记录对进行专家审查,验证了链接方法,并确定了最佳相似度值.
主要成果:
- 权重平均相似性算法实现了最高的性能,在0.82.8的值下,具有100%的特异性和99%的灵敏性.
- 这种方法成功将121,771名独特的患者联系起来,显示出96%的灵敏度,78%的特异性和89%的准确性.
- 在链接之后,人口统计数据的完整性得到了显著改善,缺少的种族数据从79%减少到11%,种族数据从82%减少到17%.
结论:
- 一个新的加权平均相似性算法有效地连接不同的EHR和EDR系统,克服互操作性挑战.
- 开发的算法提高了患者数据的完整性,促进了更好的临床决策支持和跨学科的护理.
- 这种联系提供了医学和牙科之间的基础桥梁,使更安全的程序,及时的转诊和综合研究成为可能.
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