Evaluation of the Impact of an Innovative Immunization Practice Model Designed to Improve Population Health: Results of the Project IMPACT Immunizations Pilot
Bibliographic record
Abstract
The goal of the initiative was to evaluate the impact of an innovative practice model on identification of unmet vaccination needs and vaccination rates. This was accomplished through a prospective, multisite, observational study in 8 community pharmacy practices with adults receiving an influenza vaccine with a documented vaccination forecast review from October 22, 2015 through March 22, 2016. When patients presented for influenza vaccinations, pharmacists utilized immunization information systems (IIS) data at the point of care to identify unmet vaccination needs, educate patients, and improve vaccination rates. The main outcome measures were the number of vaccination forecast reviews, patients educated, unmet vaccination needs identified and resolved, and vaccines administered. Pharmacists reviewed vaccination forecasts generated by clinical decision-support technology based on patient information documented in the IIS for 1080 patients receiving influenza vaccinations. The vaccination forecasts predicted there were 1566 additional vaccinations due at the time patients were receiving the influenza vaccine. Pharmacist assessments identified 36 contraindications and 196 potential duplications, leaving a net of 1334 unmet vaccination needs eligible for vaccination. In all, 447 of the 1334 unmet vaccinations needs were resolved during the 6-month study period, and the remainder of patients received information about their vaccination needs and recommendations to follow up for their vaccinations. Integration of streamlined principle-centered processes of care in immunization practices that allow pharmacists to utilize actionable point-of-care data resulted in identification of unmet vaccination needs, education of patients about their vaccination needs, a 41.4% increase in the number of vaccines administered, and significant improvements in routinely recommended adult vaccination rates.
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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 | 0.037 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".