Improving Systemic Sclerosis Quality of Care
Bibliographic record
Abstract
OBJECTIVE: Systemic sclerosis (SSc) is a multisystemic autoimmune disease with high morbidity and healthcare costs. Inconsistent quality of care delivery, including inadequate screening and monitoring, necessitates improvement. This study aimed to enhance the uptake of validated quality indicators (QIs) for SSc. METHODS: An interrupted time series study was conducted at 4 scleroderma clinics across 2 hospitals using the Model for Improvement methodology, employing Plan-Do-Study-Act (PDSA) cycles. A retrospective chart review assessed baseline frequencies of selected QIs. The primary aim was to increase rates of 7 baseline and 5 follow-up QIs to 80%. Root-cause analysis identified barriers to QI uptake, leading to interventions including provider education, equipment procurement, and care standardization with reminder systems. Real-time data tracking was facilitated through run charts. RESULTS: The average completion rate for baseline QIs increased from 48% to 83% over 8 months, with sustained improvements post-PDSA cycle 3. Monitoring and treatment QI completion improved from 40% to 77%. Process measures saw increases in completion rates: baseline spirometry and diffusing lung capacity for carbon monoxide rates improved from 63.5% to 92%, documented counseling to perform weekly blood pressure self-measurement increased from 19% to 86.6%, referrals to hand range-of-motion exercise programs rose from 54% to 92%, baseline creatine kinase measurement rates increased from 52% to 88%, and oxygen saturation documentation rose from 31% to 65%. Stakeholders reported high satisfaction (median rating of 4), with minimal additional time per patient (median 2.5 minutes). CONCLUSION: This QI study significantly improved SSc care through low-cost, applicable interventions, setting a precedent for future work on long-term sustainability and broader application in chronic disease management.
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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 | 0.017 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".