Improving Pneumococcal Vaccination Rates in Rheumatology Patients by Using Best Practice Alerts in the Electronic Health Records
Bibliographic record
Abstract
OBJECTIVE: To improve pneumococcal vaccination (PV) rates among rheumatology clinic patients on immunosuppressive therapy in the outpatient settings. METHODS: This quality improvement project was based on the pre-post intervention design. Phase I of the project targeted patients with rheumatoid arthritis from 13 rheumatology clinics (January 2013-July 2015) on immunosuppressive therapy to receive the pneumococcal polysaccharide vaccine (PPSV23). In the Phase II study (January 2016-October 2017), all patients on immunosuppressive medications regardless of diagnosis were targeted to receive PPSV23 and the pneumococcal conjugate vaccine (PCV13). The best practice alerts (BPAs) for both PVs were developed based on the Centers for Disease Control and Prevention guidelines, which appeared on electronic medical records for eligible patients at the time of assessment by the medical assistant. The BPA was designed to inform the vaccination status and enable the physician to order the PV, or to document refusal or deferral reasons. Education regarding vaccine guidelines, BPAs, vaccination process, and regular feedback of results were important project interventions. The vaccination rates during pre-post intervention for each study phase were compared using chi-square test. RESULTS: < 0.0001). The documentation rates (vaccine received, ordered, patient refusal and deferral reasons) increased significantly in both phases. CONCLUSION: Electronic identification of vaccine eligibility and implementation of BPAs with capabilities to order and document resulted in significantly improved PV rates. The process has 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 | 0.014 | 0.079 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".