ReviewBMJ digital health & AI2026
Harnessing patient health data to benefit individuals and advance research: a focus on rheumatoid arthritis - a narrative review.
Review in BMJ digital health & AI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
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
Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterised by fluctuating symptoms, progressive functional decline and substantial impacts on quality of life. Despite therapeutic advances, clinical management often relies on infrequent, clinic-based assessments that may fail to capture daily variability in pain, fatigue, mobility and treatment response. Increasingly, real-world data-including patient-reported outcomes, electronic health records, registries and digital health technologies-offer opportunities to characterise disease burden better. This narrative review examines the evolving role of real-world evidence (RWE) in RA with three objectives: (1) identify current applications, (2) examine persistent barriers and (3) explore actionable pathways to enhance the utility of RWE. Collectively, these approaches position RWE to complement randomised trials through the wider adoption of pragmatic trial designs, target trial emulation and digital health technologies to generate regulatory-grade evidence. By integrating and optimising patient-generated health data, the healthcare system can identify and calculate hidden costs that are currently not captured by traditional metrics. This provides a clear policy rationale for revamping health policy and healthcare delivery, ensuring appropriate resource utilisation and significant healthcare savings. To bridge the gap from clinical trial to practice, a coordinated dialogue across multiple stakeholders is required to establish patient-centred data sovereignty and governance models, ensuring patients are the primary beneficiaries of the digital health revolution.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.