Extraction of Rheumatoid Arthritis Disease Activity Measures From Electronic Health Records Using Automated Processing Algorithms. Issue 10 (30th October 2019)

• Record Type: Journal Article
• Authors: Cannon, Grant W.; Rojas, Jorge; Reimold, Andreas; Mikuls, Ted R.; Bergman, Debra; Sauer, Brian C.
• Is Part Of: ACR open rheumatology. Volume 1:Issue 10(2019)
• Page Start: 632
• Page End: 639
• Publication Date: 2019-10-30
• Subjects: Rheumatology -- Periodicals; 616.723005
• DOI: 10.1002/acr2.11089
• Languages: English
• ISSNs: 2578-5745

Abstract

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.