Identification of Gaps in Quality of Care and Good Practice Interventions in Rheumatoid Arthritis: Insights From a Literature Review and Qualitative Study of Nine Centers in North America
Bibliographic record
Abstract
OBJECTIVE: Quality of care (QoC) delivery in rheumatoid arthritis (RA) continues to suffer from various challenges (eg, delay in diagnosis and referral) that can lead to poor patient outcomes. This study aimed to identify good practice interventions that address these challenges in RA care in North America. METHODS: The study was conducted in three steps: (1) literature review of existing publications and guidelines (April 2005 to April 2021) on QoC in RA; (2) in-person visits to >50 individual specialists and health care professionals across nine rheumatology centers in the United States and Canada to identify challenges in RA care and any corresponding good practice interventions; and (3) collation and organization of findings of the two previous methods by commonalities to identify key good practice interventions, followed by further review by RA experts to ensure key challenges and gaps in RA care were captured. RESULTS: Several challenges and eight good practice interventions were identified in RA care. The interventions were prioritized based on the perceived positive impact on the challenges in care and ease of implementation. High-priority interventions included the use of technology to improve care, streamlining specialist treatment, and facilitating comorbidity assessment and care. Other interventions included enabling patient access to optimal medication regimens and improving patient self-management strategies. CONCLUSION: Learnings from the study can be implemented in other rheumatology centers throughout North America to improve RA care. Although the study was completed before the COVID-19 pandemic, the findings remain relevant.
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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 | 0.064 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| 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".