A Multifaceted Intervention to Improve Influenza, Pneumococcal, and Herpes Zoster Vaccination among Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: Vaccination rates for influenza, pneumococcus, and zoster in patients with rheumatoid arthritis (RA) have remained low. Simple electronic or paper reminders have produced only small increases in vaccination rates. We sought to identify a more effective approach to improve vaccination rates. METHODS: We conducted a system-level intervention at an academic rheumatology clinic that included electronic reminders with linked order sets, physician auditing and feedback, patient outreach, and optional printed prescriptions for zoster vaccination at an outside pharmacy. RESULTS: We targeted 1255 eligible patients with RA. There was no change in patients' self-reported influenza vaccination rates, although the baseline self-reported rate was already high and much higher than that documented in the electronic health record. Pneumococcal vaccination rates increased from 28.7% to 45.8%; in regression analysis, the rate of change in pneumococcal vaccination increased by 9.4% per year above baseline trends (95% CI 3.9-15.5, p = 0.002). The rate of zoster vaccination increased from 2.5% to 4.5% overall (p = 0.01) and from 3.0% to 6.6% among patients not receiving biologic therapy that precluded zoster vaccination. CONCLUSION: Although the intervention improved pneumococcal and zoster vaccination rates, the improvement in pneumococcal vaccination rate was less than expected, and the zoster vaccination rate remained low even for ideal candidates. Likely barriers include lack of familiarity and difficulty using electronic reminders and order sets, uncertainty about the value and safety of recommended vaccines, and uncertainty about patients' insurance coverage and prior vaccination history. Future interventions should include strategies to address these.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".