Feasibility of Measurement and Adherence to System Performance Measures for Rheumatoid Arthritis in 5 Models of Care
Bibliographic record
Abstract
OBJECTIVE: To test the feasibility of reporting on 4 national performance measures for patients with rheumatoid arthritis (RA) in 5 different models of care. METHODS: The following performance measures were evaluated in 5 models of care: waiting time (WT) to rheumatologist consultation, percentage of patients seen in yearly followup (FU), percentage taking disease-modifying antirheumatic drugs (DMARD), and time to starting DMARD. All models aimed to improve early access and care for patients with RA. RESULTS: A number of feasibility issues were encountered in performance measure evaluation because of differences in site data collection and/or the duration of the model of care. For example, while 4/5 programs maintained clinical or research databases, chart reviews were still required to report on WT. Median WT for care in 2015 varied by site between 21 and 75 days. Yearly FU rates could only be calculated in 2 sites (combined owing to small numbers) and varied between 83% and 100%. Percentage of patients taking a DMARD and time to DMARD could be calculated in 3 models, and rates of DMARD use were between 90% and 100%, with median time to DMARD of 0 days in each. CONCLUSION: Our review has shown that even in models of care designed to improve access to care and early treatment, data to document improvements are often lacking. Where data were available for measuring, deficits in WT performance were noted for some centers. Our results highlight a need to improve reporting processes to drive quality improvement.
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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 | 0.367 | 0.475 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".