Dataset Information

Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms.

ABSTRACT:

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.

SUBMITTER: Cannon GW

PROVIDER: S-EPMC6917327 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms.

Cannon Grant W GW Rojas Jorge J Reimold Andreas A Mikuls Ted R TR Bergman Debra D Sauer Brian C BC

ACR open rheumatology 20191030 10

<h4>Objective</h4>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).<h4>Methods</h4>An automated text processing system was developed to extract the Disease Activity Score for 28 joints (DAS28) and its clinical and laboratory elements ...[more]

PMID: 31872185

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Dataset Information

Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms.

Objective

Methods

Results

Conclusion

Publications

Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms.

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