Food entries in a large allergy data repository

Joseph M Plasek; Foster R Goss; Kenneth H Lai; Jason J Lau; Diane L Seger; Kimberly G Blumenthal; Paige G Wickner; Sarah P Slight; Frank Y Chang; Maxim Topaz; David W Bates; Li Zhou

doi:10.1093/jamia/ocv128

Food entries in a large allergy data repository

J Am Med Inform Assoc. 2016 Apr;23(e1):e79-87. doi: 10.1093/jamia/ocv128. Epub 2015 Sep 17.

Authors

Affiliations

¹ Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA.
² Department of Emergency Medicine and Clinical Decision Making, Tufts Medical Center, Boston, MA, USA Department of Emergency Medicine, University of Colorado, Aurora, CO, USA.
³ Clinical & Quality Analysis, Partners HealthCare System, Boston, MA, USA.
⁴ Allergy and Immunology, Massachusetts General Hospital, Boston, MA, USA.
⁵ Division of Rheumatology, Immunology, and Allergy, Brigham and Women's Hospital, Boston, MA, USA.
⁶ Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA Division of Pharmacy, School of Medicine, Pharmacy and Health, Durham University, Durham, UK.
⁷ Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA.
⁸ Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA Harvard Medical School, Boston, MA, USA.
⁹ Division of General Internal Medicine and Primary Care, Brigham and Women's Hospital, Boston, MA, USA Clinical Informatics, Partners eCare, Partners HealthCare System, Boston, MA, USA Harvard Medical School, Boston, MA, USA lzhou2@partners.org.

Abstract

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.

Keywords: allergy and immunology; controlled; electronic health records; food hypersensitivity; natural language processing; systematized nomenclature of medicine; vocabulary.

Publication types

Research Support, U.S. Gov't, P.H.S.

MeSH terms

Allergens
Databases as Topic*
Food Hypersensitivity*
Humans
Natural Language Processing
Systematized Nomenclature of Medicine
Terminology as Topic*
Vocabulary, Controlled

Substances

Allergens

Abstract

Publication types

MeSH terms

Substances

Grants and funding