学习健康系统的关键:信息交换和一个共同的数据模型
Aaron S Eisman1,2,3, Elizabeth S Chen1,2,4, Wen-Chih Wu2,4,5
1Center for Biomedical Informatics, Brown University, Providence, RI 02912, United States.
Journal of the American Medical Informatics Association : JAMIA
|November 14, 2024
概括
标准化为共同数据模型 (HIE-CDM) 的健康信息交换可以改善学习健康系统 (LHS) 的数据流. 这种方法可以实现更好的人口健康研究和干预.
科学领域:
- 医疗信息学 医疗信息学
- 人口健康管理 人口健康管理
- 学习健康系统 学习健康系统
背景情况:
- 中央管理的健康信息交换 (HIEs) 汇总来自多个合作伙伴的数据.
- 将HIE数据标准化为共同数据模型 (CDM) 对于互操作性至关重要.
- 学习健康系统 (LHS) 需要无的语义数据流来进行持续改进.
研究的目的:
- 展示健康信息交换共同数据模型 (HIE-CDM) 如何促进学习健康系统 (LHS) 的语义数据流.
- 展示标准化HIE数据在观测性人口健康研究中的潜力.
- 为了说明利用现有的医疗IT基础设施促进LHS进步.
主要方法:
- 在47个合作伙伴中运行全州HIE汇总数据,来自该州一半以上的人口.
- 将标准化HIE数据转化为观察医学结果伙伴关系 (OMOP) 共同数据模型 (CDM).
- 从2013-2023年分析了动脉样硬化心血管疾病 (ASCVD) 风险和初级预防实践.
主要成果:
- 计算了62,999个人的纵向10年ASCVD风险,揭示了许多数据合作伙伴的风险因素.
- 启用了个人ASCVD风险,初级预防 (他类药物治疗) 和疾病发病率的粒度跟踪.
- 在联邦合格卫生中心患者中发现了低于最佳的他类药物坚持,并确定了ASCVD风险概况和他类药物的使用差异. 通过CDM转换统一数据,减少异质性.
结论:
- 证明了HIE-CDM在支持观察性人口健康研究方面的潜力.
- 通过利用现有的卫生IT基础设施和数据最佳实践,展示了克服LHS障碍的能力.
- 确认HIE-CDM在多个层面 (个人,卫生系统,人口) 促进知识策划和干预开发.
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