Harnessing of real-world data and real-world evidence using digital tools: utility and potential models in rheumatology practice. (16th September 2021)

• Record Type: Journal Article
• Authors: Kataria, Suchitra; Ravindran, Vinod
• Is Part Of: Rheumatology. Volume 61:Number 2(2022)
• Page Start: 502
• Page End: 513
• Publication Date: 2021-09-16
• Subjects: big data -- data analytics -- artificial intelligence -- electronic health records -- machine learning; Rheumatism -- Periodicals; Rheumatology -- Periodicals; 616.723005
• DOI: 10.1093/rheumatology/keab674
• Languages: English
• ISSNs: 1462-0324

Abstract

Abstract: The diversity of diseases in rheumatology and variability in disease prevalence necessitates greater data parity in disease presentation, treatment responses including adverse events to drugs and various comorbidities. Randomized controlled trials are the gold standard for drug development and performance evaluation. However, when the drug is applied outside the controlled environment, the outcomes may differ in patient populations. In this context, the need to understand the macro and micro changes involved in disease evolution and progression becomes important and so is the need for harvesting and harnessing the real-world data from various resources to use them in generating real-world evidence. Digital tools with potential relevance to rheumatology can potentially be leveraged to obtain greater patient insights, greater information on disease progression and disease micro processes and even in the early diagnosis of diseases. Since the patients spend only a minuscule portion of their time in hospital or in a clinic, using modern digital tools to generate realistic, bias-proof, real-world data in a non-invasive patient-friendly manner becomes critical. In this review we have appraised different digital mediums and mechanisms for collecting real-world data and proposed digital care models for generating real-world evidence in rheumatology.