A value set for documenting adverse reactions in electronic health records
A value set for documenting adverse reactions in electronic health records
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DOI：
10.1093/jamia/ocx139
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发表时间：
2018-06-01
影响因子：
6.4
通讯作者：
Zhou, Li
中科院分区：
文献类型：
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作者：
Goss, Foster R.;Lai, Kenneth H.;Zhou, Li
目的：建立记录和编码电子病历过敏反应模块中不良反应的综合值集。材料和方法：我们分析了存储在Partner Healthcare的企业范围过敏信息库(PEAR)中的270万名患者的2 471 004例不良反应。使用医学文本提取、推理和映射系统，我们处理了结构化和自由文本反应条目，并将它们映射到系统化的医学术语-临床术语。我们计算了反应概念的频率，包括罕见、严重和超敏反应。我们将PEAR概念与联邦卫生信息建模和标准值集以及内布拉斯加州大学医学中心的数据进行比较，然后创建一个集成值集。结果：我们在PEAR中识别出787个反应概念。常见的不良反应包括：皮疹(14.0%)、麻疹(8.2%)、胃肠道刺激(5.5%)、瘙痒(3.2%)和过敏反应(2.5%)。我们从联邦卫生信息建模和标准以及内布拉斯加州大学医学中心确定了另外320个概念，以解决在将这些外部资源与PEAR进行比较时因缺失和部分匹配而造成的差距。这在我们的最终综合价值集中产生了1106个概念。在这两个外部数据集中，罕见、严重和超敏反应的存在都是有限的。超敏反应约占我们数据中反应的20%。讨论：我们开发了一个用于编码不良反应的值集，使用来自一个卫生系统的大数据集，丰富了来自两个大型外部资源的反应。该综合值集包括临床上重要的严重和超敏反应。结论：本工作提供了一个与现有数据协调的值集，以提高电子健康记录中反应记录的一致性和准确性，为更智能地为过敏和不良反应提供临床决策支持提供必要的构建块。
Objective: To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record.Materials and Methods: We analyzed 2 471 004 adverse reactions stored in Partners Healthcare's Enterprise-wide Allergy Repository (PEAR) of 2.7 million patients. Using the Medical Text Extraction, Reasoning, and Mapping System, we processed both structured and free-text reaction entries and mapped them to Systematized Nomenclature of Medicine -Clinical Terms. We calculated the frequencies of reaction concepts, including rare, severe, and hypersensitivity reactions. We compared PEAR concepts to a Federal Health Information Modeling and Standards value set and University of Nebraska Medical Center data, and then created an integrated value set.Results: We identified 787 reaction concepts in PEAR. Frequently reported reactions included: rash (14.0%), hives (8.2%), gastrointestinal irritation (5.5%), itching (3.2%), and anaphylaxis (2.5%). We identified an additional 320 concepts from Federal Health Information Modeling and Standards and the University of Nebraska Medical Center to resolve gaps due to missing and partial matches when comparing these external resources to PEAR. This yielded 1106 concepts in our final integrated value set. The presence of rare, severe, and hypersensitivity reactions was limited in both external datasets. Hypersensitivity reactions represented roughly 20% of the reactions within our data.Discussion: We developed a value set for encoding adverse reactions using a large dataset from one health system, enriched by reactions from 2 large external resources. This integrated value set includes clinically important severe and hypersensitivity reactions.Conclusion: This work contributes a value set, harmonized with existing data, to improve the consistency and accuracy of reaction documentation in electronic health records, providing the necessary building blocks for more intelligent clinical decision support for allergies and adverse reactions.