P048 Assessing the health equity impacts of smartphone-based remote monitoring in people with rheumatoid arthritis: a stakeholder engagement workshop
Bibliographic record
Abstract
Abstract Background/Aims REmote Monitoring of Rheumatoid Arthritis (REMORA) is a smartphone-app for daily symptom-tracking which is linked to the electronic health record, aiming to improve clinical decision-making and disease activity in people with rheumatoid arthritis (RA). Given well-established sociodemographic inequities which influence access to smartphones and the ability to utilise and benefit from them for health purposes, we engaged with stakeholder-representatives to assess the potential impact of REMORA on health equity and identify mitigation strategies. Methods We organised a 3-hour stakeholder-engagement workshop, inviting people with RA, healthcare professionals (HCPs) and digital health researchers. The workshop was guided by the validated Health Equity Impact Assessment-Digital Health Supplement (HEIA-DH), aiming to explore for REMORA: Groups at-risk of digital health inequities; Potential inadvertent positive/negative impacts; Strategies to mitigate negative impacts. We captured suggestions on a virtual whiteboard and kept field-notes using a pre-defined template for thematic analysis. Results Table 1 presents the workshop results. We identified multiple groups at-risk of digital health inequities (e.g., people with physical disabilities; non-native English speakers; people with information-processing difficulties) for whom stakeholders also highlighted unintended positive impacts (e.g., reduced travel to appointments; supporting verbal discussions with HCPs; opportunities to prepare for consultations). Suggested strategies to mitigate negative impacts included: ensuring app compatibility with larger devices; early explanation of medical terminology; adapting data-inputting/data-summary design features. Conclusion Our results highlight important considerations relevant to the wider field of smartphone-based remote monitoring for long-term conditions. In addition to identifying sociodemographic groups at-risk of digital health inequities, we recognised risks for groups with specific health problems such as physical/sensory disabilities. Our proposed mitigation strategies can be broadly classified into: app adaptations; considerations during app prescription; sign-posting users towards sources of support. We also found that whilst REMORA aims to improve communication between all patients and HCPs, there may be an unexpected additional benefit for groups with specific communication challenges. Our findings demonstrate the utility of HEIA-DH in assessing the potential health equity impact of smartphone-based remote monitoring; further workshops are planned with specific at-risk groups. Regular re-evaluation will help gauge the effectiveness of our strategies and identify further unanticipated equity impacts of REMORA. Disclosure M. Al-Attar: None. A. Crawford: None. A. Gambin: None. S.M. Ali: None. W.G. Dixon: Consultancies; W.G.D. has received consultancy from Google, unrelated to this work. S.N. van der Veer: None.
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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 | 0.040 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".