Telemedicine in the management of rheumatoid arthritis: maintaining disease control with less health-care utilization
Bibliographic record
Abstract
Abstract Objectives We aimed to evaluate the use of an eHealth platform and a self-management outpatient clinic in patients with RA in a real-world setting. The effects on health-care utilization and disease activity were studied. Methods Using hospital data of patients with RA between 2014 and 2019, the use of an eHealth platform and participation in a self-management outpatient clinic were studied. An interrupted time series analysis compared the period before and after the introduction of the eHealth platform. The change in trend (relative to the pre-interruption trend) for the number of outpatient clinic visits and the DAS for 28 joints (DAS28) were determined for several scenarios. Results After implementation of the platform in April 2017, the percentage of patients using it was stable at ∼37%. On average, the users of the platform were younger, more highly educated and had better health outcomes than the total RA population. After implementation of the platform, the mean number of quarterly outpatient clinic visits per patient decreased by 0.027 per quarter (95% CI: −0.045, −0.08, P = 0.007). This was accompanied by a significant decrease in DAS28 of 0.056 per quarter (95% CI: −0.086, −0025, P = 0.001). On average, this resulted in 0.955 fewer visits per patient per year and a reduction of 0.503 in the DAS28. Conclusion The implementation of remote patient monitoring has a positive effect on health-care utilization, while maintaining low disease activity. This should encourage the use of this type of telemedicine in the management of RA, especially while many routine outpatient clinic visits are cancelled owing to COVID-19.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".