Reducing Missed Opportunities for Influenza Vaccination in Patients with Rheumatoid Arthritis: Evaluation of a Multisystem Intervention
Bibliographic record
Abstract
OBJECTIVE: To assess a multimodal intervention for reducing missed opportunities for outpatient influenza vaccination in individuals with rheumatoid arthritis (RA). METHODS: Patients with RA were enrolled from a single center and each rheumatology outpatient visit was tracked for missed opportunities for influenza vaccination, defined as a visit in which an unvaccinated patient without contraindications remained unvaccinated or lacked documentation of vaccine recommendation in the electronic medical record (EMR). Providers then received a multimodal intervention consisting of an education session, EMR alerts, and weekly provider-specific e-mail reminders. Missed opportunities before and after the intervention were compared, and the determinants of missed opportunities were analyzed. RESULTS: A total of 228 patients with RA were enrolled (904 preintervention visits) and 197 returned for at least 1 postintervention visit (721 postintervention visits). The preintervention frequency of any missed opportunities for influenza vaccination was 47%. This was reduced to 23% postintervention (p < 0.001). Among those vaccinated, the relative hazard for influenza vaccination post- versus preintervention period was 1.24 (p = 0.038). Younger age, less frequent office visits, higher erythrocyte sedimentation rate, and negative attitudes about vaccines were each independently associated with missed opportunities preintervention. Postintervention, these factors were no longer associated with missed opportunities; however, the intervention was not as effective in non-Hispanic black patients, non-English speakers, those residing outside of the New York City metropolitan area, and those reporting prior adverse reactions to vaccines. CONCLUSION: Improved uptake of influenza vaccination in patients with RA is possible using a multimodal approach. Certain subgroups may need a more potent intervention for equivalent efficacy.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".