Remote monitoring of patients with rheumatoid arthritis in a low disease activity state: a mixed methods evaluation across six hospitals in London, UK
Bibliographic record
Abstract
OBJECTIVES: This study evaluated the scale-up of a remote monitoring service, capturing monthly Rheumatoid Arthritis Impact of Disease scores and patient-generated text messages, for patients with rheumatoid arthritis (RA; in remission or with low disease activity) attending routine outpatient clinics across six hospitals. We explored patients and staff experiences and implementation outcomes. METHODS: A pragmatic, mixed methods approach was used, with active patient involvement throughout. We undertook a rapid review, analysed service-level data, and conducted a patient survey and patient and staff interviews, informed by the Capability, Opportunity, Motivation, Behaviour (COM-B) and Exploration, Preparation, Implementation, Sustainment (EPIS) theoretical frameworks. RESULTS: The review included 37 articles, covering themes of patient and clinician acceptability, engagement, feasibility and clinical impact. Service-level data (n = 202) showed high levels of patient engagement with the service. The patient survey (n = 155) showed patients felt the service was easy to use, had confidence in it and felt it improved access to care. Patient interview (n = 22) findings mirrored those of the survey. Motivating factors included increased responsiveness and ease of contact with clinical teams. Views from staff interviews (n = 16) were more mixed. Some implementation barriers were specific to roll-out sites. Prioritization of staff needs was emphasized. CONCLUSION: Patients were positive about the service and engagement was high. Staff views and engagement were more mixed. Results suggest that equal levels of patient and staff engagement are required for sustainability. These findings further our understanding of the implementation challenges to scaling remote monitoring interventions for patients with rheumatoid arthritis in routine care settings.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.022 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".