A180: A Population Management Tool for Proactive Care of Juvenile Idiopathic Arthritis in the Pediatric Rheumatology Care and Outcomes Improvement Network
Bibliographic record
Abstract
Background/Purpose: The Pediatric Rheumatology Care and Outcomes Improvement Network (PR‐COIN) is a multi‐center, quality‐improvement‐focused learning network with a mission of improving outcomes for children and adolescents with juvenile idiopathic arthritis (JIA). PR‐COIN has developed a population management tool which facilitates pro‐active, coordinated health care communications and interventions that can be used in managing JIA patient populations. Methods: The population management tool was developed with information technology expertise to link seamlessly to the PR‐COIN longitudinal registry. Within the population management tool, on demand reports can be generated using participating site or aggregate, network level data according to demographics, clinical features, disease activity levels, patient reported outcomes (functional ability/CHAQ score, arthritis related pain), medication use, and quality indicators. Drill down capability enables identification of patients. Results: Currently, data from 8 sites, 1161 patients, and 5334 clinical encounters are included in the population management tool (Table ). Individual site usage of the population management tool is variable ranging from infrequent use to regular use at monthly population management meetings. The PR‐COIN network is currently developing strategies to promote consistent usage of the tool at sites. Clinicians routinely using information provided through this tool report satisfaction with its functionality and ease of use; furthermore they report enhanced ability to proactively identify and address specific patient needs prior to and during clinic visits, optimizing care. Aggregate patient data report from PR‐COIN population management tool Characteristic N = 1146 Age (years) < 2 4 (0.3%) 2–4 84 (7.6%) 5–7 148 (12.9%) 8–12 324 (28.3%) > 12 583 (50.9%) JIA subtype Oligoarticular persistent 236 (20.6%) Oligoarticular extended 132 (11.5%) Polyarticular, RF negative 396 (34.5%) Polyarticular, RF positive 99 (8.6%) Enthesitis related arthritis 63 (5.5%) Systemic 75 (6.5%) Undifferentiated 25 (2.2%) Disease status Clinically inactive disease on medications 264 (35.9%) Active uveitis 71 (6.2%) Prior history of uveitis, currently inactive 100 (8.7%) Physician global assessment (PGA) = 0–3 991 (86.4%) Optimal physical function 507 (57.1%) Pain score = 0 482 (42%) Other measures Compliant with uveitis screening 351 (30.6%) On biologic medication 418 (36.4%) Per Wallace criteria; PGA scale 0–10 CHAQ = 0; per Hellinghaus guidelines Conclusion: The population management tool allows real‐time feedback to sites regarding overall JIA population and individual patient status. Customized, “on demand” population measurement and quality improvement reports generated by the network and participating sites and can be used to identify gaps in patient care and at‐risk subpopulations that may benefit from more intensive or between‐visit care. Current efforts include developing a care stratification scores that can assist in identifying at risk JIA subpopulations and provision of individual patient reports that can be used to assist in pre‐visit planning. Future goals include providing feedback to individual patients about their disease status compared to local and network aggregate data, and customized educational and interventional tools that can be provided to JIA patients and families. Thus, the PR‐COIN JIA population management tool can be leveraged to improve JIA network and site performance and individual patient care.
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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 | 0.013 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".