Patient-reported outcomes in RA care improve patient communication, decision-making, satisfaction and confidence: qualitative results
Bibliographic record
Abstract
OBJECTIVE: To evaluate the impact of integrating patient-reported outcomes (PROs) into routine clinics, from the perspective of patients with RA, clinicians and other staff. METHODS: We conducted a prospective cohort study using a mixed methods sequential explanatory design at an academic arthritis clinic. RA patients completed selected Patient-Reported Outcomes Measurement Information System measures on tablets in the waiting room. Results were immediately available to discuss during the visit. Post-visit surveys with patients and physicians evaluated topics discussed and their impact on decision making; patients rated confidence in treatment. Focus groups or interviews with patients, treating rheumatologists and clinic staff were conducted to understand perspectives and experiences. RESULTS: Some 196 patients and 20 rheumatologists completed post-visit surveys at 816 and 806 visits, respectively. Focus groups were conducted with 24 patients, 10 rheumatologists and 4 research/clinic staff. PROs influenced medical decision-making and RA treatment changes (38 and 18% of visits, respectively). Patients reported very high satisfaction and treatment confidence. Impact on clinical workflow was minimal after a period of initial adjustment. PROs were valued by patients and physicians, and provided new insight into how patients felt and functioned over time. Reviewing results together improved communication, and facilitated patient-centred care, shared decision making, and the identification of new symptoms and contributing psychosocial/behavioural factors. CONCLUSION: PRO use at RA visits was feasible, increased understanding of how disease affects how patients feel and function, facilitated shared decision-making, and was associated with high patient satisfaction and treatment confidence.
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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 | 0.021 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".