Evaluating key performance indicators of the process of care in juvenile idiopathic arthritis
Bibliographic record
Abstract
OBJECTIVE: To determine whether and how often the information to measure a set of key performance indicators (KPIs) in juvenile idiopathic arthritis (JIA) is found in data collected routinely in a Pediatric Rheumatology Clinic. METHODS: A retrospective electronic chart review and administrative data analysis was conducted for a cohort of 140 patients with JIA at a tertiary Pediatric Rheumatology Clinic between 2016-2020. The set of KPIs include measuring patient outcomes (joint assessment, physician's global assessment of disease activity, assessment of functional ability, composite disease activity measurement), access to care (waiting time between referral and first visit, visit with the rheumatologist within the first year of diagnosis, annual follow-up visits with the rheumatologist), and safety (tuberculosis screening, and laboratory monitoring). Documentation was assessed as a binary variable indicating whether the required information was ever found. Documentation frequency for each KPI was assessed with counts and percentages of the number of times the required information was documented for each clinic visit. Compliance with the safety KPI definitions was assessed using administrative databases. RESULTS: Data for each KPI were found at least once in the cohort and documentation varied in frequency and consistency. Access to care and safety KPIs were documented more frequently than patient outcome KPIs. A joint assessment was documented at every visit for 95% of patients, 46% for an assessment of pain, and none for a physician's global assessment of disease activity, an assessment of functional ability, or a composite disease activity measurement. CONCLUSION: Although feasible to measure, there is an opportunity for improving the consistency of documentation. Having an active system of monitoring KPIs and tools to simplify measurement is a key step in the process toward improved patient care outcomes. Streamlining the collection of KPI data can increase the likelihood of compliance. Next steps should involve replicating this study in various centres.
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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 | 0.026 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".