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Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing

BACKGROUND: Drug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to...

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Autores principales: Lo, Ying-Chih, Varghese, Sheril, Blackley, Suzanne, Seger, Diane L., Blumenthal, Kimberly G., Goss, Foster R., Zhou, Li
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234873/
https://www.ncbi.nlm.nih.gov/pubmed/35769562
http://dx.doi.org/10.3389/falgy.2022.904923
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author Lo, Ying-Chih
Varghese, Sheril
Blackley, Suzanne
Seger, Diane L.
Blumenthal, Kimberly G.
Goss, Foster R.
Zhou, Li
author_facet Lo, Ying-Chih
Varghese, Sheril
Blackley, Suzanne
Seger, Diane L.
Blumenthal, Kimberly G.
Goss, Foster R.
Zhou, Li
author_sort Lo, Ying-Chih
collection PubMed
description BACKGROUND: Drug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to inaccurate allergy labels, inappropriate drug-allergy alerts, and potentially ineffective, more toxic, and/or costly care. In this study, we used natural language processing (NLP) to automatically detect discrepancies between the EHR allergy list and drug challenge test results and to inform the clinical recommendations provided in a real-time allergy reconciliation module. METHODS: This study included patients who received drug challenge tests at the Mass General Brigham (MGB) Healthcare System between June 9, 2015 and January 5, 2022. At MGB, drug challenge tests are performed in allergy/immunology encounters with routine clinical documentation in notes and flowsheets. We developed a rule-based NLP tool to analyze and interpret the challenge test results. We compared these results against EHR allergy lists to detect potential discrepancies in allergy documentation and form a recommendation for reconciliation if a discrepancy was identified. To evaluate the capability of our tool in identifying discrepancies, we calculated the percentage of challenge test results that were not updated and the precision of the NLP algorithm for 200 randomly sampled encounters. RESULTS: Among 200 samples from 5,312 drug challenge tests, 59% challenged penicillin reactivity and 99% were negative. 42.0%, 61.5%, and 76.0% of the results were confirmed by flowsheets, NLP, or both, respectively. The precision of the NLP algorithm was 96.1%. Seven percent of patient allergy lists were not updated based on drug challenge test results. Flowsheets alone were used to identify 2.0% of these discrepancies, and NLP alone detected 5.0% of these discrepancies. Because challenge test results can be recorded in both flowsheets and clinical notes, the combined use of NLP and flowsheets can reliably detect 5.5% of discrepancies. CONCLUSION: This NLP-based tool may be able to advance global delabeling efforts and the effectiveness of drug allergy assessments. In the real-time EHR environment, it can be used to examine patient allergy lists and identify drug allergy label discrepancies, mitigating patient risks.
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spelling pubmed-92348732022-06-28 Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing Lo, Ying-Chih Varghese, Sheril Blackley, Suzanne Seger, Diane L. Blumenthal, Kimberly G. Goss, Foster R. Zhou, Li Front Allergy Allergy BACKGROUND: Drug challenge tests serve to evaluate whether a patient is allergic to a medication. However, the allergy list in the electronic health record (EHR) is not consistently updated to reflect the results of the challenge, affecting clinicians' prescription decisions and contributing to inaccurate allergy labels, inappropriate drug-allergy alerts, and potentially ineffective, more toxic, and/or costly care. In this study, we used natural language processing (NLP) to automatically detect discrepancies between the EHR allergy list and drug challenge test results and to inform the clinical recommendations provided in a real-time allergy reconciliation module. METHODS: This study included patients who received drug challenge tests at the Mass General Brigham (MGB) Healthcare System between June 9, 2015 and January 5, 2022. At MGB, drug challenge tests are performed in allergy/immunology encounters with routine clinical documentation in notes and flowsheets. We developed a rule-based NLP tool to analyze and interpret the challenge test results. We compared these results against EHR allergy lists to detect potential discrepancies in allergy documentation and form a recommendation for reconciliation if a discrepancy was identified. To evaluate the capability of our tool in identifying discrepancies, we calculated the percentage of challenge test results that were not updated and the precision of the NLP algorithm for 200 randomly sampled encounters. RESULTS: Among 200 samples from 5,312 drug challenge tests, 59% challenged penicillin reactivity and 99% were negative. 42.0%, 61.5%, and 76.0% of the results were confirmed by flowsheets, NLP, or both, respectively. The precision of the NLP algorithm was 96.1%. Seven percent of patient allergy lists were not updated based on drug challenge test results. Flowsheets alone were used to identify 2.0% of these discrepancies, and NLP alone detected 5.0% of these discrepancies. Because challenge test results can be recorded in both flowsheets and clinical notes, the combined use of NLP and flowsheets can reliably detect 5.5% of discrepancies. CONCLUSION: This NLP-based tool may be able to advance global delabeling efforts and the effectiveness of drug allergy assessments. In the real-time EHR environment, it can be used to examine patient allergy lists and identify drug allergy label discrepancies, mitigating patient risks. Frontiers Media S.A. 2022-05-10 /pmc/articles/PMC9234873/ /pubmed/35769562 http://dx.doi.org/10.3389/falgy.2022.904923 Text en Copyright © 2022 Lo, Varghese, Blackley, Seger, Blumenthal, Goss and Zhou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Allergy
Lo, Ying-Chih
Varghese, Sheril
Blackley, Suzanne
Seger, Diane L.
Blumenthal, Kimberly G.
Goss, Foster R.
Zhou, Li
Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing
title Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing
title_full Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing
title_fullStr Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing
title_full_unstemmed Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing
title_short Reconciling Allergy Information in the Electronic Health Record After a Drug Challenge Using Natural Language Processing
title_sort reconciling allergy information in the electronic health record after a drug challenge using natural language processing
topic Allergy
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9234873/
https://www.ncbi.nlm.nih.gov/pubmed/35769562
http://dx.doi.org/10.3389/falgy.2022.904923
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