Comparing the variability of ingredient, strength, and dose form information from electronic prescriptions with RxNorm drug product descriptions
To determine the variability of ingredient, strength, and dose form information from drug product descriptions in real-world electronic prescription (e-prescription) data. A sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named ent...
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Veröffentlicht in: | Journal of the American Medical Informatics Association : JAMIA 2022-08, Vol.29 (9), p.1471-1479 |
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creator | Lester, Corey A Flynn, Allen J Marshall, Vincent D Rochowiak, Scott Rowell, Brigid Bagian, James P |
description | To determine the variability of ingredient, strength, and dose form information from drug product descriptions in real-world electronic prescription (e-prescription) data.
A sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named entity extraction model and National Drug Codes (NDCs) were used to get RxNorm Concept Unique Identifiers (RxCUI) via RxNorm. The number of drug product description variants for each RxCUI was determined. Variants identified were compared to RxNorm to determine the extent of matching terminology used.
A total of 353 002 unique pairs of drug product descriptions and NDCs were analyzed. The median (1st-3rd quartile) number of variants extracted for each standardized expression in RxNorm, was 3 (2-7) for ingredients, 4 (2-8) for strength, and 41 (11-122) for dosage forms. Of the pairs, 42.35% of ingredients (n = 328 032), 51.23% of strengths (n = 321 706), and 10.60% of dose forms (n = 326 653) used matching terminology, while 16.31%, 24.85%, and 13.05% contained nonmatching terminology, respectively.
A wide variety of drug product descriptions makes it difficult to determine whether 2 drug product descriptions describe the same drug product (eg, using abbreviations to describe an active ingredient or using different units to represent a concentration). This results in patient safety risks that lead to incorrect drug products being ordered, dispensed, and used by patients. Implementation and use of standardized terminology may reduce these risks.
Drug product descriptions on real-world e-prescriptions exhibit large variation resulting in unnecessary ambiguity and potential patient safety risks. |
doi_str_mv | 10.1093/jamia/ocac096 |
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A sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named entity extraction model and National Drug Codes (NDCs) were used to get RxNorm Concept Unique Identifiers (RxCUI) via RxNorm. The number of drug product description variants for each RxCUI was determined. Variants identified were compared to RxNorm to determine the extent of matching terminology used.
A total of 353 002 unique pairs of drug product descriptions and NDCs were analyzed. The median (1st-3rd quartile) number of variants extracted for each standardized expression in RxNorm, was 3 (2-7) for ingredients, 4 (2-8) for strength, and 41 (11-122) for dosage forms. Of the pairs, 42.35% of ingredients (n = 328 032), 51.23% of strengths (n = 321 706), and 10.60% of dose forms (n = 326 653) used matching terminology, while 16.31%, 24.85%, and 13.05% contained nonmatching terminology, respectively.
A wide variety of drug product descriptions makes it difficult to determine whether 2 drug product descriptions describe the same drug product (eg, using abbreviations to describe an active ingredient or using different units to represent a concentration). This results in patient safety risks that lead to incorrect drug products being ordered, dispensed, and used by patients. Implementation and use of standardized terminology may reduce these risks.
Drug product descriptions on real-world e-prescriptions exhibit large variation resulting in unnecessary ambiguity and potential patient safety risks.</description><identifier>ISSN: 1527-974X</identifier><identifier>ISSN: 1067-5027</identifier><identifier>EISSN: 1527-974X</identifier><identifier>DOI: 10.1093/jamia/ocac096</identifier><identifier>PMID: 35773948</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Drug Prescriptions ; Electronic Prescribing ; Humans ; Research and Applications ; RxNorm ; Vocabulary, Controlled</subject><ispartof>Journal of the American Medical Informatics Association : JAMIA, 2022-08, Vol.29 (9), p.1471-1479</ispartof><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com 2022</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c387t-456e47d6db69d62e5bf99e623fbbec1bedcc552af959f438636fa34b31e167593</citedby><cites>FETCH-LOGICAL-c387t-456e47d6db69d62e5bf99e623fbbec1bedcc552af959f438636fa34b31e167593</cites><orcidid>0000-0001-8774-793X</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382370/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC9382370/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,727,780,784,885,27923,27924,53790,53792</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/35773948$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lester, Corey A</creatorcontrib><creatorcontrib>Flynn, Allen J</creatorcontrib><creatorcontrib>Marshall, Vincent D</creatorcontrib><creatorcontrib>Rochowiak, Scott</creatorcontrib><creatorcontrib>Rowell, Brigid</creatorcontrib><creatorcontrib>Bagian, James P</creatorcontrib><title>Comparing the variability of ingredient, strength, and dose form information from electronic prescriptions with RxNorm drug product descriptions</title><title>Journal of the American Medical Informatics Association : JAMIA</title><addtitle>J Am Med Inform Assoc</addtitle><description>To determine the variability of ingredient, strength, and dose form information from drug product descriptions in real-world electronic prescription (e-prescription) data.
A sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named entity extraction model and National Drug Codes (NDCs) were used to get RxNorm Concept Unique Identifiers (RxCUI) via RxNorm. The number of drug product description variants for each RxCUI was determined. Variants identified were compared to RxNorm to determine the extent of matching terminology used.
A total of 353 002 unique pairs of drug product descriptions and NDCs were analyzed. The median (1st-3rd quartile) number of variants extracted for each standardized expression in RxNorm, was 3 (2-7) for ingredients, 4 (2-8) for strength, and 41 (11-122) for dosage forms. Of the pairs, 42.35% of ingredients (n = 328 032), 51.23% of strengths (n = 321 706), and 10.60% of dose forms (n = 326 653) used matching terminology, while 16.31%, 24.85%, and 13.05% contained nonmatching terminology, respectively.
A wide variety of drug product descriptions makes it difficult to determine whether 2 drug product descriptions describe the same drug product (eg, using abbreviations to describe an active ingredient or using different units to represent a concentration). This results in patient safety risks that lead to incorrect drug products being ordered, dispensed, and used by patients. Implementation and use of standardized terminology may reduce these risks.
Drug product descriptions on real-world e-prescriptions exhibit large variation resulting in unnecessary ambiguity and potential patient safety risks.</description><subject>Drug Prescriptions</subject><subject>Electronic Prescribing</subject><subject>Humans</subject><subject>Research and Applications</subject><subject>RxNorm</subject><subject>Vocabulary, Controlled</subject><issn>1527-974X</issn><issn>1067-5027</issn><issn>1527-974X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2022</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpVkU9v1DAQxS0EoqVw5Ip85NC0dhzb8QUJrfhTqQIJgcTNcuzxrqskDrZT6LfgI-OlS7WcZjTvN88ePYReUnJBiWKXN2YK5jJaY4kSj9Ap5a1slOy-Pz7qT9CznG8IoaJl_Ck6YVxKprr-FP3exGkxKcxbXHaAb2trhjCGcoejx3WcwAWYyznOJcG8LbtzbGaHXcyAfUxTZfbFlBBn7FOcMIxgS4pzsHhJkG0Ky17M-GcoO_zl16f9lkvrtsrRrbZgd0Q9R0-8GTO8ONQz9O39u6-bj8315w9Xm7fXjWW9LE3HBXTSCTcI5UQLfPBKQT3PDwNYOoCzlvPWeMWV71gvmPCGdQOjQIXkip2hN_e-yzpMla43JjPqJYXJpDsdTdD_K3PY6W281Yr1LZOkGrw-GKT4Y4Vc9BSyhXE0M8Q161b0HSWUdLKizT1qU8w5gX94hhK9T1H_TVEfUqz8q-O_PdD_YmN_AAUooOo</recordid><startdate>20220816</startdate><enddate>20220816</enddate><creator>Lester, Corey A</creator><creator>Flynn, Allen J</creator><creator>Marshall, Vincent D</creator><creator>Rochowiak, Scott</creator><creator>Rowell, Brigid</creator><creator>Bagian, James P</creator><general>Oxford University Press</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0001-8774-793X</orcidid></search><sort><creationdate>20220816</creationdate><title>Comparing the variability of ingredient, strength, and dose form information from electronic prescriptions with RxNorm drug product descriptions</title><author>Lester, Corey A ; Flynn, Allen J ; Marshall, Vincent D ; Rochowiak, Scott ; Rowell, Brigid ; Bagian, James P</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c387t-456e47d6db69d62e5bf99e623fbbec1bedcc552af959f438636fa34b31e167593</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Drug Prescriptions</topic><topic>Electronic Prescribing</topic><topic>Humans</topic><topic>Research and Applications</topic><topic>RxNorm</topic><topic>Vocabulary, Controlled</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lester, Corey A</creatorcontrib><creatorcontrib>Flynn, Allen J</creatorcontrib><creatorcontrib>Marshall, Vincent D</creatorcontrib><creatorcontrib>Rochowiak, Scott</creatorcontrib><creatorcontrib>Rowell, Brigid</creatorcontrib><creatorcontrib>Bagian, James P</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lester, Corey A</au><au>Flynn, Allen J</au><au>Marshall, Vincent D</au><au>Rochowiak, Scott</au><au>Rowell, Brigid</au><au>Bagian, James P</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Comparing the variability of ingredient, strength, and dose form information from electronic prescriptions with RxNorm drug product descriptions</atitle><jtitle>Journal of the American Medical Informatics Association : JAMIA</jtitle><addtitle>J Am Med Inform Assoc</addtitle><date>2022-08-16</date><risdate>2022</risdate><volume>29</volume><issue>9</issue><spage>1471</spage><epage>1479</epage><pages>1471-1479</pages><issn>1527-974X</issn><issn>1067-5027</issn><eissn>1527-974X</eissn><abstract>To determine the variability of ingredient, strength, and dose form information from drug product descriptions in real-world electronic prescription (e-prescription) data.
A sample of 10 399 324 e-prescriptions from 2019 to 2021 were obtained. Drug product descriptions were analyzed with a named entity extraction model and National Drug Codes (NDCs) were used to get RxNorm Concept Unique Identifiers (RxCUI) via RxNorm. The number of drug product description variants for each RxCUI was determined. Variants identified were compared to RxNorm to determine the extent of matching terminology used.
A total of 353 002 unique pairs of drug product descriptions and NDCs were analyzed. The median (1st-3rd quartile) number of variants extracted for each standardized expression in RxNorm, was 3 (2-7) for ingredients, 4 (2-8) for strength, and 41 (11-122) for dosage forms. Of the pairs, 42.35% of ingredients (n = 328 032), 51.23% of strengths (n = 321 706), and 10.60% of dose forms (n = 326 653) used matching terminology, while 16.31%, 24.85%, and 13.05% contained nonmatching terminology, respectively.
A wide variety of drug product descriptions makes it difficult to determine whether 2 drug product descriptions describe the same drug product (eg, using abbreviations to describe an active ingredient or using different units to represent a concentration). This results in patient safety risks that lead to incorrect drug products being ordered, dispensed, and used by patients. Implementation and use of standardized terminology may reduce these risks.
Drug product descriptions on real-world e-prescriptions exhibit large variation resulting in unnecessary ambiguity and potential patient safety risks.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>35773948</pmid><doi>10.1093/jamia/ocac096</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-8774-793X</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Drug Prescriptions Electronic Prescribing Humans Research and Applications RxNorm Vocabulary, Controlled |
title | Comparing the variability of ingredient, strength, and dose form information from electronic prescriptions with RxNorm drug product descriptions |
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