ArticleJournal of the American Medical Informatics Association : JAMIA
Automated identification of drug and food allergies entered using non-standard terminology.
Article in Journal of the American Medical Informatics Association : JAMIA. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- The Use of Electronic Health Records to Study Drug-Induced Hypersensitivity Reactions from 2000 to 2021: A Systematic Review.Immunology and allergy clinics of North America · 2022Pooled it
- Extracting social determinants of health from electronic health records using natural language processing: a systematic review.Journal of the American Medical Informatics Association : JAMIA · 2021Pooled it
- Development of a text mining algorithm for identifying adverse drug reactions in electronic health records.JAMIA open · 2024Article
- Identifying adverse drug reactions from free-text electronic hospital health record notes.British journal of clinical pharmacology · 2022Article
- Multi-label classification of symptom terms from free-text bilingual adverse drug reaction reports using natural language processing.PloS one · 2022Article
- Improving Allergy Documentation: A Retrospective Electronic Health Record System-Wide Patient Safety Initiative.Journal of patient safety · 2022Article
- Artificial intelligence approaches using natural language processing to advance EHR-based clinical research.The Journal of allergy and clinical immunology · 2020Review
- AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2020Article
- Integrating terminologies into standard SQL: a new approach for research on routine data.Journal of biomedical semantics · 2019Article
- Review
- Clinical Informatics Researcher's Desiderata for the Data Content of the Next Generation Electronic Health Record.Applied clinical informatics · 2017Article
- Leveraging Food and Drug Administration Adverse Event Reports for the Automated Monitoring of Electronic Health Records in a Pediatric Hospital.Biomedical informatics insights · 2017Article
- Creation of an Accurate Algorithm to Detect Snellen Best Documented Visual Acuity from Ophthalmology Electronic Health Record Notes.JMIR medical informatics · 2016Article
- Food entries in a large allergy data repository.Journal of the American Medical Informatics Association : JAMIA · 2016Article
- Desiderata for computable representations of electronic health records-driven phenotype algorithms.Journal of the American Medical Informatics Association : JAMIA · 2015Article
- An evaluation of a natural language processing tool for identifying and encoding allergy information in emergency department clinical notes.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2014Article
- Induced lexico-syntactic patterns improve information extraction from online medical forums.Journal of the American Medical Informatics Association : JAMIAArticle
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Authors and funding
6 authors.
Funding
Abstract
objectiveAn 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. MATERIALS AND
methodsAn 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).
resultsAccuracy, 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. DISCUSSION: 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.
conclusionsA high performing, easily maintained algorithm can successfully identify medication and food allergies from free text entries in EHR systems.
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