Using Process Improvement and Systems Redesign to Improve Rheumatology Care Quality in a Safety Net Clinic
Bibliographic record
Abstract
OBJECTIVE: To develop and evaluate interventions to improve quality of care in 4 priority areas in an urban safety net adult rheumatology clinic serving a racially/ethnically and socioeconomically diverse patient population. METHODS: The Institute for Healthcare Improvement's Model for Improvement was used to redesign clinical processes to achieve prespecified benchmarks in the following areas from 2015 to 2017: 13-valent pneumococcal conjugate vaccine (PCV13) administration among immunocompromised patients; disease activity monitoring with the Clinical Disease Activity Index (CDAI) for patients with rheumatoid arthritis; latent tuberculosis infection (LTBI) screening for new biologic users with RA; and reproductive health counseling among women receiving potentially teratogenic medications. We measured performance for each using standardized metrics, defined as the proportion of eligible patients receiving recommended care. RESULTS: There were 1205 patients seen in the clinic between 2015 and 2017. Regarding demographics, 71% were women, 88% identified as racial/ethnic minorities, and 45% were eligible for at least 1 of the quality measures. Shewart charts for the PCV13 and CDAI measures showed evidence of improved healthcare delivery over time. Benchmarks were achieved for the CDAI and LTBI measures with 93% and 91% performance, respectively. Performance for the PCV13 and reproductive health counseling measures was 78% and 46%, respectively, but did not meet prespecified improvement targets. CONCLUSION: Through an interprofessional approach, we were able to achieve durable improvements in key rheumatology quality measures largely by enhancing workflow, engaging nonphysician providers, and managing practice variation.
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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 | 0.032 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".