ArticleACR open rheumatology2019
Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms.
Article in ACR open rheumatology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Large language model-based extraction of rheumatoid arthritis clinical disease activity index from clinical notes.JAMIA open · 2026Article
- Heterogeneity in the association of genetic risk for RA and resultant RA phenotypes.Rheumatology (Oxford, England) · 2026Article
- Genetically-determined variation in CRP impacts disease activity assessment in rheumatoid arthritis.Rheumatology (Oxford, England) · 2025Observational
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThe 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).
methodsAn 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.
resultsIn 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.
conclusionThe 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.
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Registered trials
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