Evaluation of the Scale up of Remote Monitoring in Rheumatology Outpatients Across Three NHS Trusts in South East London, UK: Study Protocol
Bibliographic record
Abstract
Abstract Background Modern treat-to-target approaches to rheumatoid arthritis (RA) involve frequent monitoring of disease activity with the goal of disease remission or a low disease activity. The Rheumatoid Arthritis Impact of Disease (RAID) is a multidimensional, validated patient-reported outcome measure that covers seven domains, which has been found to discriminate between active and non-active disease. Applying smartphone apps to monitoring of RA is described as an innovation which has been implemented in the UK and USA. The proposed study will evaluate the feasibility of scaled implementation of a remote monitoring service based on RAID for eligible patients with RA at three NHS organisations (trusts) in south east London, UK. Methods Pragmatic formative service evaluation study informed by implementation theory and incorporating the perspectives of RA service users throughout. The study will follow a multi-method approach. Rapid evidence review will be carried out to identify implementation approaches used in similar services. Quantitative data will be collected from a cross-sectional sample of service users through a web-based questionnaire assessing patient satisfaction, as well as service-level data routinely collected by trusts and from the remote monitoring system and documentation produced in developing and implementing the remote monitoring service. Qualitative data will be collected from approximately 30 clinical and non-clinical staff and 20-30 patients purposively sampled to conduct semi-structured interviews to explore their perspectives on remote monitoring in RA. The evaluation will be supported by established implementation frameworks, including EPIS (Exploration, Preparation, Implementation and Sustainment) and COM-B (Capability-Opportunity-Motivation-Behaviour), which will be used to guide data generation and to inform the framework analysis of qualitative data. Discussion This pragmatic study will enhance the understanding of implementation process and outcomes and will explore the potential to scale up the remote monitoring system in RA. A larger scale hybrid study can be designed based on the dataset the current study will produce to offer definitive clinical and implementation evaluation.
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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 | 0.087 | 0.064 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".