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Lookup NU author(s): Professor Sarah SlightORCiD
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© The Author 2015.Objective: Accurate food adverse sensitivity documentation in electronic health records (EHRs) is crucial to patient safety. This study examined, encoded, and grouped foods that caused any adverse sensitivity in a large allergy repository using natural language processing and standard terminologies. Methods: Using the Medical Text Extraction, Reasoning, and Mapping System (MTERMS), we processed both structured and free-text entries stored in an enterprise-wide allergy repository (Partners' Enterprise-wide Allergy Repository), normalized diverse food allergen terms into concepts, and encoded these concepts using the Systematized Nomenclature of Medicine - Clinical Terms (SNOMED-CT) and Unique Ingredient Identifiers (UNII) terminologies. Concept coverage also was assessed for these two terminologies. We further categorized allergen concepts into groups and calculated the frequencies of these concepts by group. Finally, we conducted an external validation of MTERMS's performance when identifying food allergen terms, using a randomized sample from a different institution. Results: We identified 158 552 food allergen records (2140 unique terms) in the Partners repository, corresponding to 672 food allergen concepts. High-frequency groups included shellfish (19.3%), fruits or vegetables (18.4%), dairy (9.0%), peanuts (8.5%), tree nuts (8.5%), eggs (6.0%), grains (5.1%), and additives (4.7%). Ambiguous, generic concepts such as "nuts" and "seafood" accounted for 8.8% of the records. SNOMED-CT covered more concepts than UNII in terms of exact (81.7% vs 68.0%) and partial (14.3% vs 9.7%) matches. Discussion Adverse sensitivities to food are diverse, and existing standard terminologies have gaps in their coverage of the breadth of allergy concepts. Conclusion: New strategies are needed to represent and standardize food adverse sensitivity concepts, to improve documentation in EHRs
Author(s): Plasek JM, Goss FR, Lai KH, Lau JJ, Seger DL, Blumenthal KG, Wickner PG, Slight SP, Chang FY, Topaz M, Bates DW, Zhou L
Publication type: Article
Publication status: Published
Journal: Journal of the American Medical Informatics Association
Year: 2016
Volume: 23
Issue: e1
Pages: 79-87
Print publication date: 01/04/2016
Online publication date: 17/09/2015
Acceptance date: 10/07/2015
ISSN (print): 1067-5027
ISSN (electronic): 1527-974X
Publisher: Oxford University Press
URL: https://doi.org/10.1093/jamia/ocv128
DOI: 10.1093/jamia/ocv128
PubMed id: 26384406
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