Department of Emergency Medicine, University of Colorado, Aurora, CO, USA.
Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, UT, USA.
J Am Med Inform Assoc. 2018 Jun 1;25(6):661-669. doi: 10.1093/jamia/ocx139.
To develop a comprehensive value set for documenting and encoding adverse reactions in the allergy module of an electronic health record.
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.
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.
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.
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.
开发一个综合的价值集，用于记录和编码电子健康记录中过敏模块的不良反应。
我们分析了 270 万患者的 Partners Healthcare 全范围过敏知识库 (PEAR) 中存储的 247.1 万例不良反应。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的反应条目，并将其映射到医学术语系统命名法。我们计算了反应概念的频率，包括罕见、严重和过敏反应。我们将 PEAR 概念与联邦健康信息建模和标准值集以及内布拉斯加大学医学中心的数据进行了比较，然后创建了一个综合值集。
我们在 PEAR 中确定了 787 个反应概念。经常报告的反应包括：皮疹（14.0%）、荨麻疹（8.2%）、胃肠刺激（5.5%）、瘙痒（3.2%）和过敏反应（2.5%）。我们从联邦健康信息建模和标准以及内布拉斯加大学医学中心中确定了另外 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产生的差距。这使得我们的最终综合值集中有 1106 个概念。在这两个外部数据集，罕见、严重和过敏反应的存在都很有限。过敏反应约占我们数据中反应的 20%。
我们使用来自一个医疗系统的大型数据集，通过来自两个大型外部资源的反应来丰富，开发了一个用于编码不良反应的价值集。这个综合的价值集包括临床上重要的严重和过敏反应。
这项工作贡献了一个与现有数据相协调的价值集，以提高电子健康记录中反应记录的一致性和准确性，为过敏和不良反应的更智能临床决策支持提供必要的构建块。
