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Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms
OBJECTIVE: The accurate and efficient collection and documentation of disease activity measures (DAMs) is critical to improve clinical care and outcomes research in rheumatoid arthritis (RA). This study evaluated the performance of an automated process to extract DAMs from medical notes in the elect...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917327/ https://www.ncbi.nlm.nih.gov/pubmed/31872185 http://dx.doi.org/10.1002/acr2.11089 |
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author | Cannon, Grant W. Rojas, Jorge Reimold, Andreas Mikuls, Ted R. Bergman, Debra Sauer, Brian C. |
author_facet | Cannon, Grant W. Rojas, Jorge Reimold, Andreas Mikuls, Ted R. Bergman, Debra Sauer, Brian C. |
author_sort | Cannon, Grant W. |
collection | PubMed |
description | OBJECTIVE: The accurate and efficient collection and documentation of disease activity measures (DAMs) is critical to improve clinical care and outcomes research in rheumatoid arthritis (RA). This study evaluated the performance of an automated process to extract DAMs from medical notes in the electronic health record (EHR). METHODS: An automated text processing system was developed to extract the Disease Activity Score for 28 joints (DAS28) and its clinical and laboratory elements from the Veterans Affairs EHR for patients enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry. After automated text processing derivation, data accuracy was assessed by comparing the automated text processing system and manual extraction with gold standard chart review in a separate validation phase. RESULTS: In the validation phase, 1569 notes from 596 patients at 3 sites were evaluated, with 75 (6%) notes detected only by automated text processing, 85 (5%) detected only by manual extraction, and 1408 (90%) detected by both methods. The accuracy of automated text processing ranged from 90.7% to 96.7% and the accuracy of manual extraction ranged from 91.3% to 95.0% for the different clinical and laboratory elements. The accuracy of the two methods to calculate the DAS28 was 78.1% for automated text processing and 78.3% for manual extraction. CONCLUSION: The automated text processing approach is highly efficient and performed as well as the manual extraction approach. This advance has the potential for significant improvements in the collection, documentation, and extraction of these data to support clinical practice and outcomes research relevant to RA as well as the potential for broader application to other health conditions. |
format | Online Article Text |
id | pubmed-6917327 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-69173272019-12-23 Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms Cannon, Grant W. Rojas, Jorge Reimold, Andreas Mikuls, Ted R. Bergman, Debra Sauer, Brian C. ACR Open Rheumatol Original Articles OBJECTIVE: The accurate and efficient collection and documentation of disease activity measures (DAMs) is critical to improve clinical care and outcomes research in rheumatoid arthritis (RA). This study evaluated the performance of an automated process to extract DAMs from medical notes in the electronic health record (EHR). METHODS: An automated text processing system was developed to extract the Disease Activity Score for 28 joints (DAS28) and its clinical and laboratory elements from the Veterans Affairs EHR for patients enrolled in the Veterans Affairs Rheumatoid Arthritis (VARA) registry. After automated text processing derivation, data accuracy was assessed by comparing the automated text processing system and manual extraction with gold standard chart review in a separate validation phase. RESULTS: In the validation phase, 1569 notes from 596 patients at 3 sites were evaluated, with 75 (6%) notes detected only by automated text processing, 85 (5%) detected only by manual extraction, and 1408 (90%) detected by both methods. The accuracy of automated text processing ranged from 90.7% to 96.7% and the accuracy of manual extraction ranged from 91.3% to 95.0% for the different clinical and laboratory elements. The accuracy of the two methods to calculate the DAS28 was 78.1% for automated text processing and 78.3% for manual extraction. CONCLUSION: The automated text processing approach is highly efficient and performed as well as the manual extraction approach. This advance has the potential for significant improvements in the collection, documentation, and extraction of these data to support clinical practice and outcomes research relevant to RA as well as the potential for broader application to other health conditions. John Wiley and Sons Inc. 2019-10-30 /pmc/articles/PMC6917327/ /pubmed/31872185 http://dx.doi.org/10.1002/acr2.11089 Text en Published 2019. This article is a U.S. Government work and is in the public domain in the USA. ACR Open Rheumatology published by Wiley Periodicals, Inc. on behalf of American College of Rheumatology. This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | Original Articles Cannon, Grant W. Rojas, Jorge Reimold, Andreas Mikuls, Ted R. Bergman, Debra Sauer, Brian C. Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms |
title | Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms |
title_full | Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms |
title_fullStr | Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms |
title_full_unstemmed | Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms |
title_short | Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms |
title_sort | extraction of rheumatoid arthritis disease activity measures from electronic health records using automated processing algorithms |
topic | Original Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6917327/ https://www.ncbi.nlm.nih.gov/pubmed/31872185 http://dx.doi.org/10.1002/acr2.11089 |
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