Use of Coordinator Role Improves Access to Rheumatologic Advanced Therapy
Bibliographic record
Abstract
OBJECTIVE: Delays in initiation of advanced therapies, which include biologics and targeted synthetic disease-modifying antirheumatic drugs, contribute to poor patient outcomes. The objective of this quality improvement project was to identify factors that lead to a delay in the initiation of advanced therapy and to perform plan-do-study-act cycles to decrease the time to start advanced therapy. METHODS: A retrospective chart review identified factors involved in delay to start advanced therapy. The primary outcome of the study was the number of days to advanced therapy start as measured by the date of rheumatologist recommendation to the date advanced therapy was initiated by the patient. An Advanced Therapy Coordinator role was created to standardize the workflow, optimize communication, and ensure a safety checklist was instituted. RESULTS: A total of 125 patients were reviewed for the study with 18 excluded. Preintervention median wait time was 82.0 (IQR 46.0-80.5) days. Median wait time during the intervention improved to 49.5 (IQR 34.0-69.5) days (April 2021 to January 2022), with nonrandom variation post intervention. Nonrandom variation was also noted in the latter baseline data (March 2020 to March 2021). CONCLUSION: This study demonstrates improved wait time to advanced therapy initiation through the role of an Advanced Therapy Coordinator to facilitate communication pathways.
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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 | 0.009 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".