Improvement in Herpes Zoster Vaccination in Patients with Rheumatoid Arthritis: A Quality Improvement Project
Bibliographic record
Abstract
OBJECTIVE: To improve herpes zoster (HZ) vaccination rates in high-risk patients with rheumatoid arthritis (RA) being treated with immunosuppressive therapy. METHODS: This quality improvement project was based on the pre- and post-intervention design. The project targeted all patients with RA over the age of 60 years while being treated with immunosuppressive therapy (not with biologics) seen in 13 rheumatology outpatient clinics. The study period was from July 2012 to June 2013 for the pre-intervention and February 2014 to January 2015 for the post-intervention phase. The electronic best practice alert (BPA) for HZ vaccination was developed; it appeared on electronic medical records during registration and medication reconciliation of the eligible patient by the medical assistant. The BPA was designed to electronically identify patient eligibility and to enable the physician to order the vaccine or to document refusal or deferral reason. Education regarding vaccine guidelines, BPA, vaccination process, and feedback were crucial components of the project interventions. The vaccination rates were compared using the chi-square test. RESULTS: We evaluated 1823 and 1554 eligible patients with RA during the pre-intervention and post-intervention phases, respectively. The HZ vaccination rates, reported as patients vaccinated among all eligible patients, improved significantly from the pre-intervention period of 10.1% (184/1823) to 51.7% (804/1554) during the intervention phase (p < 0.0001). The documentation rates (vaccine received, vaccine ordered, patient refusal, and deferral reasons) increased from 28% (510/1823) to 72.9% (1133/1554; p < 0.0001). The HZ infection rates decreased significantly from 2% to 0.3% (p = 0.002). CONCLUSION: Electronic identification of vaccine eligibility and BPA significantly improved HZ vaccination rates. The process required minimal modification of clinic work flow and did not burden the physician's time, and has the potential for self-sustainability and generalizability.
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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 | 0.035 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".