Improving Quality of Care in Rheumatoid Arthritis Through Mobile Patient-Reported Outcome Measurement: Focus Group Study
Bibliographic record
Abstract
BACKGROUND: Patient-reported outcomes (PROs) for chronic disease management can be integrated into the routine workflow by leveraging mobile technology. OBJECTIVE: The objective of our study was to describe the process of our quality improvement (QI) efforts using tablets for PRO collection in a busy, academic rheumatology practice to support a treat-to-target (TTT) approach for rheumatoid arthritis (RA) management. METHODS: Our QI team designed a process for routine collection of PROs for RA patients at the Arthritis Center, employing information technology and an electronic medical record (EMR) system. Patients received a tablet at the clinic check-in desk to complete the Routine Assessment of Patient Index Data 3 (RAPID3) survey, a validated RA PRO. RAPID3 scores were uploaded to the EMR in real time and available for use in shared decision making during routine office visits. Weekly data were collected on RAPID3 completion rates and shared with front desk staff and medical assistants to drive improvement. Patients in our patient family advisory council and focus groups provided informal feedback on the process. RESULTS: From May 1, 2017, to January 31, 2019, a total of 4233 RAPID3 surveys were completed by 1691 patients. The mean age of patients was 63 (SD 14) years; 84.00% (1420/1691) of the patients were female, and 83.00% (1403/1691) of the patients were white. The rates of RAPID3 completion increased from 14.3% (58/405) in May 2017 to 68.00% (254/376) in September 2017 and were sustained over time through January 2019. Informal feedback from patients was positive and negative, relating to the usability of the tablet and the way rheumatologists used and explained the RAPID3 data in shared decision making during the office visit. CONCLUSIONS: We designed a sustainable and reliable process for collecting PROs from patients with RA in the waiting room and integrated these data through the EMR during office visits.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".