Provider-level rates of HEDIS-consistent HPV vaccination in a regional health plan
Bibliographic record
Abstract
Background. Health insurers are well-positioned to address low HPV vaccination coverage in the US through initiatives such as provider assessment and feedback. However, little is known about the feasibility of using administrative claims data to assess provider performance on vaccine delivery.Methods. We used administrative claims data from a regional health plan to estimate provider performance on the 2013–2015 Healthcare Effectiveness Data and Information Set (HEDIS) measure for HPV vaccine. This measure required that a girl receive three doses of HPV vaccine by age 13. Providers who administered ≥1 dose in a HEDIS-consistent series received credit for meeting the goal.Results. From January 2008-April 2015, 1,975 (8.5%) of 11–12 year-old girls in our sample received a HEDIS-consistent HPV vaccine series. Our sample of providers consisted of 1,236 who had ≥10 well-visits with different female patients, and 94% of these were pediatricians. A substantial minority of providers (39.4%) did not administer any HEDIS-consistent HPV vaccine doses. Only 5.5% of providers administered HPV vaccine doses that were part of a HEDIS-consistent series to at least one-quarter of their patients. These estimates did not vary by provider sex or age. Doses in a HEDIS-consistent vaccine series were often attributed to multiple providers.Conclusions. In a regional health plan, only 5.5% of providers in our sample administered doses that were part of a complete, three-dose HPV vaccine series to at least one-quarter of their 11–12 year-old female patients.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".