Consistency of Uses of ICD Codes for Retrospective Data Analysis

背景：国际疾病分类第9次修订的临床修改(CM) (ICD)代码一直是使用健康记录数据的临床、操作和研究活动的标准 美国几十年来一直如此。2015年10月1日，医疗保险和医疗补助服务中心(CMS)取代了 ICD-9 CM编码与ICD-10 CM编码在结构和概念上与 ICD-9厘米。在许多情况下，这两组代码之间没有精确匹配。为了实现过渡 顺利，疾病控制和预防中心(CDC)和CMS已经创建了General 等价映射(GEM)或“人行横道”，可以将一个代码集转换为另一个代码集。然而，创业板 不会以完全可靠的方式简单地自动将一种代码转换为另一种代码。 意义/影响：卫生服务研究依赖于对ICD代码的准确和可靠使用。 使用现有电子病历数据的回溯性分析假设ICD代码相对一致 临床数据的表示。ICD-9 cm和ICD-9之间缺乏自动和可靠的转换- 10 CM已被证明导致对疾病患病率的错误估计，这可能导致严重的 队列识别、统计分析或机器学习模型中的错误。 创新：现有的人行横道工具，如GEM，完全基于术语和 ICD-9 cm和ICD-10 cm的层次。我们建议研究实际的纵向和上下文用法 EHR组ICD-9 cm和ICD-10 cm。像VA临床数据这样的大型电子病历存储库的优势 仓库(CDW)是一个长时间序列(CDW为~20年)和极其丰富的临床背景 (例如，人口统计、实验室、用药和文字说明)，以供我们检查ICD使用的一致性。特定的 目的：1)通过检测异常来评估ICD-9 cm和ICD-10 cm在VA EHR数据中使用的一致性 信号采用时间序列分析方法；2)提高ICD-9和ICD-10的一致性 在VA EHR数据中的使用情况，使用嵌入方法比较使用情况。方法：目标1 分析将使用已经在生物监测中得到验证的信号检测方法。AIM 2将使用 基于ICD-9 cm和ICD-10 cm编码到潜在语义空间的嵌入方法 使用上下文。术语和领域专家将审查结果的分层样本。 实施/下一步：这一试点项目的结果将与我们的业务伙伴在 弗吉尼亚州中央办公室。我们预期在这项试验的基础上进行进一步的研究，以开发一个方便用户的ICD 随着时间的推移，为VA和其他EHR数据集提供翻译工具和更准确的ICD映射 设施，并将这一努力从ICD扩展到其他术语。

Background: The Clinical Modification (CM) of the 9th Revision of International Classification of Diseases (ICD) codes have been the standard for clinical, operational and research activities using health record data in the U.S. for decades. On October 1, 2015, the Centers for Medicare & Medicaid Services (CMS) replaced the ICD-9CM codes with ICD-10CM codes, which are fundamentally different in structure and concepts from the ICD-9CM. In many cases, there are no exact matches between these two sets of codes. To make the transition smooth, the Centers for Disease Control and Prevention (CDC) and the CMS have created General Equivalence Mappings (GEM) or “crosswalks” that can translate one code set to the other. However, the GEM does not simply and automatically translate one code to another in a completely reliable way. Significance/Impact: Health services research relies on accurate and reliable use of ICD codes. Retrospective analyses using existing EHR data assume the ICD codes to be a relatively consistent representation of the clinical data. The lack of automated and reliable translation between ICD-9CM and ICD- 10 CM have been shown to result in incorrect estimations of disease prevalence, which may lead to serious errors in cohort identification, statistical analyses or machine learning models. Innovation: Existing crosswalk tools such as the GEM were developed solely based on the terms and hierarchy of the ICD-9CM and ICD-10CM. We propose to study the actual longitudinal and contextual usage of ICD-9CM and ICD-10CM in EHR. The advantage of a large EHR repository such as the VA clinical data warehouse (CDW) is that there is a long time series (~20 years in CDW) and extremely rich clinical context (e.g. demographic, lab, medication and text note) for us to examine the consistency of ICD usage. Specific Aims: 1) To assess the consistency of ICD-9CM and ICD-10CM usage in VA EHR data, by detecting aberrant signals using time-series analysis methods; and 2) To improve the consistency of ICD-9CM and ICD-10CM usage in VA EHR data, using embedding methods to compare usage contexts. Methodology: The Aim 1 analysis will use signal detection methods that have been validated in bio-surveillance. Aim 2 will use embedding methods to map each ICD-9CM and ICD-10CM code to a latent semantic space based on their usage context. Terminology and domain experts will review a stratified sample of the results. Implementation/Next Steps: Findings of this pilot project will be shared with our operational partners in the VA central office. We envision further investigations building on this pilot to develop a user-friendly ICD translation tool and more accurate ICD mappings for VA and other EHR datasets over time and across facilities, and extend the effort beyond ICD to other terminologies.