Pediatric Rheumatology Care and Outcomes Improvement Network's Quality Measure Set to Improve Care of Children With Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
OBJECTIVE: To describe the selection, development, and implementation of quality measures (QMs) for juvenile idiopathic arthritis (JIA) by the Pediatric Rheumatology Care and Outcomes Improvement Network (PR-COIN), a multihospital learning health network using quality improvement methods and leveraging QMs to drive improved outcomes across a JIA population since 2011. METHODS: An American College of Rheumatology-endorsed multistakeholder process previously selected initial process QMs. Clinicians in PR-COIN and parents of children with JIA collaboratively selected outcome QMs. A committee of rheumatologists and data analysts developed operational definitions. QMs were programmed and validated using patient data. Measures are populated by registry data, and performance is displayed on automated statistical process control charts. PR-COIN centers use rapid-cycle quality improvement approaches to improve performance metrics. The QMs are revised for usefulness, to reflect best practices, and to support network initiatives. RESULTS: The initial QM set included 13 process measures concerning standardized measurement of disease activity, collection of patient-reported outcome assessments, and clinical performance measures. Initial outcome measures were clinical inactive disease, low pain score, and optimal physical functioning. The revised QM set has 20 measures and includes additional measures of disease activity, data quality, and a balancing measure. CONCLUSION: PR-COIN has developed and tested JIA QMs to assess clinical performance and patient outcomes. The implementation of robust QMs is important to improve quality of care. PR-COIN's set of JIA QMs is the first comprehensive set of QMs used at the point-of-care for a large cohort of JIA patients in a variety of pediatric rheumatology practice settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".