Improving health data quality by assessing and enhancing semantic integrity

随着术语的变化，并随着时间的推移被不同的实体使用， 使用单一代码或一组代码所代表的变化和分歧。这些 范围可以从添加新的、更特殊的含义，到代码集（例如，添加另一 可能性的代码，其含义是第一次列为NEC（不属于其他地方分类）代码，以 重大版本变更（如从ICD-9 CM向ICD-10 CM的过渡），以及局部调整 (e.g.使用更通用的代码来指示机构的更具体的条件。等 改变代码的语义提出了一个挑战，可以称为代表性的 语义完整性（RS完整性）。如果多个代码或多个代码组合可以 代表相同的表型、队列识别或队列变量分配， 代码就成了问题。由于许多研究项目利用大型电子健康 包含标准化术语代码的EHR数据集，违反RS完整性 预计将在随后的分析和调查结果中传播错误。拟议 该项目旨在解决这个问题：如何评估和提高遥感完整性， 纵向和异构EHR数据使用自动化的方法？我们建议 开发新的数据驱动的方法来分析时间模式和上下文 EHR变量。使用ICD-9 CM、ICD-10 CM、CPT和SNOMED代码作为我们的用例， 研究将利用非常大的纵向异构数据集：临床数据仓库 退伍军人管理局（VA）的国家EHR系统，Cerner真实的世界数据（RWD） 和来自亚拉巴马大学的一个大型医疗中心的EHR数据存储库， 伯明翰（UAB）。我们的目标是：1）开发数据驱动的方法来评估RS 纵向EHR数据的完整性。我们将开发统计和深度学习模型， 执行多变量时间序列分析，以检测代码中的异常信号 2）开发数据驱动的方法，以提高纵向RS完整性 EHR数据。我们将分析代码随时间的变化和跨数据源的上下文， 嵌入技术，并开发一个语义匹配工具， 来自不同时间段和设施的数据的等效聚类;以及3） 评估和改进不同编码集和数据源的方法。我们 还将评估对预测建模的影响。

As terminologies change and are used over time by different entities, there can develop changes and divergence in what the use of a single code or a set of codes represent. These can range from adding a new, more special meaning, to a code set (e.g., adding another possibility to codes whose meaning was first listed as a NEC (not elsewhere classified) code, to a major version change (as in the transition from ICD-9CM to ICD-10CM), and to local adaption (e.g. using a more general code to indicate a more specific condition by an institution.) Such altering of the semantics of codes presents a challenge that can be termed representational semantic integrity (RS integrity). If multiple codes or multiple combinations of codes can represent the same phenotype, cohort identification or cohort variable assignment based on the codes becomes problematic. As numerous research projects utilize large electronic health record (EHR) datasets containing standardized terminology codes, violations of RS integrity would be expected to propagate errors in subsequent analyses and findings. The proposed project seeks to address the question: How to assess and improve RS integrity in longitudinal and heterogenous EHR data using automated methods? We propose to develop novel data driven methods to analyze the temporal pattern and the context of EHR variables. Using ICD-9CM, ICD-10CM, CPT and SNOMED codes as our use cases, this study will leverage very large, longitudinal, heterogenous datasets: the Clinical Data Warehouse of the Veteran Administration (VA)’s national EHR system, the Cerner Real World Data (RWD) and the EHR data repository from a large medical center at University of Alabama at Birmingham (UAB). Our aims are: 1) Develop data-driven approaches to assess RS integrity in longitudinal EHR data. We will develop statistical and deep learning models to perform multivariate time-series analysis for the purpose of detecting aberrant signals in codes in EHR records; 2) Develop data-driven approaches to improve RS integrity in longitudinal EHR data. We will analyze the contexts of codes over time and across data sources using embedding techniques and develop a semantic matching tool that generates semantic equivalent clusters for data from different time periods and facilities; and 3) Validate the assessment and improvement approaches on different coding sets and data sources. We will also assess the impact on predicative modeling.