The Impact of Collecting Electronic Patient-Reported Outcomes (ePROs) Between Visits on Rheumatological Care: A Scoping Review
Bibliographic record
Abstract
Objectives To synthesize evidence on the collection of electronic patient-reported outcomes (ePROs) in between visits to guide rheumatology care for patients with inflammatory arthritis (IA) and to evaluate the feasibility, facilitators, and barriers to implementing ePRO-based monitoring in routine practice. Methods A scoping review was conducted using MEDLINE and Embase databases with keywords related to “patient-reported outcome measures,” “rheumatology,” and “telehealth.” Primary studies that utilized ePROs in between visits to inform care were included. Two reviewers independently screened and extracted data, which was synthesized narratively, focusing on study characteristics, patient demographics, types of ePROs collected, how ePROs informed care, and facilitators and barriers to integration. Results Nineteen studies were included (Table 1). Study designs included randomized controlled trial or intervention design (N=9), qualitative interviews (N=5), proof of concept (N=1), prospective cohort (N=2), cross-sectional (N=1), and observational pilot study (N=1). Sample sizes ranged from 10 to 2111. Most studies came from the Netherlands (N=3), Germany (N=2), China (N=2), USA (N=2), and Denmark (N=2). Studies focused on rheumatoid arthritis (RA) (N=14), ankylosing spondylitis (N=1), systemic lupus erythematosus (N=1) and a combination of RA and spondyloarthritis (N= 3). ePRO integration was found to influence multiple aspects of rheumatological care, including reducing in-person appointments, increasing frequency of medication changes, improving medication adherence, and enhancing patient-physician communication. Four studies reported better clinical outcomes with ePRO-based monitoring, while 4 found it to be noninferior to standard care despite fewer in-person visits. Two studies showed no significant improvements in quality of life, medication intensity, or remission rates; 1 had low adherence due to daily reporting, while the other was conducted in a setting with highly standardized treatment protocols. Key facilitators of ePRO implementation included user-friendly application interfaces (N=7), features supporting patient engagement like medication management tools and personalized reminders (N=5), flexible reporting frequencies (N=3), and ability to skip unnecessary in-person visits (N=3). Identified barriers included increased patient burden due frequent completion of questionnaires (N=3) and lack of physician reimbursement for ePRO review (N=2). Table 1. Summary of study characteristics Conclusion ePROs present promising opportunities to improve clinical outcomes, patient-centered care in rheumatology, and healthcare efficiency by enabling real-time monitoring, promoting self-management, and supporting personalized treatment strategies. However, effective implementation requires a nuanced approach that considers both facilitators and barriers. The findings highlight the need for ongoing collaboration among healthcare providers, patients, and technology developers to address challenges and fully harness the potential of ePROs in enhancing outcomes and efficiency for individuals with IA.
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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 | 0.153 | 0.418 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.029 | 0.028 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.012 | 0.009 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".