Department of Anesthesiology, Jefferson Medical College, Philadelphia, Pennsylvania, USA.
J Am Med Inform Assoc. 2013 Sep-Oct;20(5):962-8. doi: 10.1136/amiajnl-2013-001756. Epub 2013 Jun 7.
An accurate computable representation of food and drug allergy is essential for safe healthcare. Our goal was to develop a high-performance, easily maintained algorithm to identify medication and food allergies and sensitivities from unstructured allergy entries in electronic health record (EHR) systems.
An algorithm was developed in Transact-SQL to identify ingredients to which patients had allergies in a perioperative information management system. The algorithm used RxNorm and natural language processing techniques developed on a training set of 24 599 entries from 9445 records. Accuracy, specificity, precision, recall, and F-measure were determined for the training dataset and repeated for the testing dataset (24 857 entries from 9430 records).
Accuracy, precision, recall, and F-measure for medication allergy matches were all above 98% in the training dataset and above 97% in the testing dataset for all allergy entries. Corresponding values for food allergy matches were above 97% and above 93%, respectively. Specificities of the algorithm were 90.3% and 85.0% for drug matches and 100% and 88.9% for food matches in the training and testing datasets, respectively.
The algorithm had high performance for identification of medication and food allergies. Maintenance is practical, as updates are managed through upload of new RxNorm versions and additions to companion database tables. However, direct entry of codified allergy information by providers (through autocompleters or drop lists) is still preferred to post-hoc encoding of the data. Data tables used in the algorithm are available for download.
A high performing, easily maintained algorithm can successfully identify medication and food allergies from free text entries in EHR systems.
准确的可计算食物和药物过敏表示对于安全的医疗保健至关重要。我们的目标是开发一种高性能、易于维护的算法，以从电子健康记录（EHR）系统中的非结构化过敏条目识别药物和食物过敏和敏感性。
在 Transact-SQL 中开发了一种算法，以识别围手术期信息管理系统中患者对哪些成分过敏。该算法使用了 RxNorm 和在 24599 个来自 9445 个记录的训练集中开发的自然语言处理技术。针对训练数据集确定了准确性、特异性、精度、召回率和 F 度量，并针对测试数据集（来自 9430 个记录的 24857 个条目）重复了这些指标。
在训练数据集和测试数据集中，药物过敏匹配的准确性、精度、召回率和 F 度量均高于 98%，所有过敏条目的药物过敏匹配均高于 97%。食物过敏匹配的相应值分别为 97%以上和 93%以上。在训练和测试数据集，药物匹配的算法特异性分别为 90.3%和 85.0%，食物匹配的特异性分别为 100%和 88.9%。
该算法在识别药物和食物过敏方面具有很高的性能。维护是可行的，因为通过上传新版本的 RxNorm 和添加到配套数据库表来管理更新。然而，与事后对数据进行编码相比，提供者直接输入编码过敏信息（通过自动完成器或下拉列表）仍然是首选。算法中使用的数据表可下载。
一种高性能、易于维护的算法可以成功地从 EHR 系统中的自由文本条目中识别药物和食物过敏。
