The Effect of Clinical Decision Prompts in Improving Human Papillomavirus Vaccination Rates in a Multispecialty Practice in a Predominantly Hispanic Population: Quasi-Experimental Study
Bibliographic record
Abstract
BACKGROUND: The human papillomavirus (HPV) is implicated in the causal pathway of cancers of the vulva, vagina, penis, cervix, anus, and oropharyngeal region. It is the most common sexually transmitted infection in the United States. Despite the documented safety and effectiveness of the HPV vaccine, rates lag behind those of other vaccines given at the same age. OBJECTIVE: Provider recommendation is identified as a robust predictor of HPV vaccine uptake, and physician-prompting is shown to increase the provision of preventive care services in general. Theoretically, providing reminders to providers should increase opportunities for providing HPV vaccine recommendations and therefore affect vaccination rates. The objective of our study was to assess the effectiveness of an electronic medical record (EMR) prompt in improving HPV vaccination rates in an academic clinic setting caring for a predominantly Hispanic border population. METHODS: We used a quasi-experimental design with a retrospective chart audit to evaluate the effect of a clinical decision prompt (CDP) on improving HPV immunization rates in different specialty settings. We introduced an EMR prompt to remind providers to recommend the HPV vaccine when seeing appropriate patients in an obstetrics and gynecology (OBGYN), pediatrics (PD), and family medicine (FM) clinic in a large multispecialty academic group located along the Texas-Mexico border. We assessed HPV vaccination rates in all the departments involved before and after introducing the prompts. Participants included male and female patients between the ages of 9 and 26 years, presenting at the clinics between January 2014 and December 2015. RESULTS: We reviewed over 2800 charts in all 3 clinics. After adjusting for age, ethnicity, race, type of insurance, preferred language, and clinic, the odds of immunization were 92% (P<.001) higher in patients after the prompt implementation of the EMR. In addition, there was an overall statistically significant increase in the overall HPV vaccination completion rates after implementing the CDP (31.96% vs 21.22%; P<.001). Again, OBGYN saw the most significant improvement in vaccination completion rates, with rates at follow-up 66.02% higher than baseline rates (P=.04). PD and FM had somewhat similar but no less impressive improvements (57.7% and 58.36%; P<.001). CONCLUSIONS: Implementing an EMR CDP improved our overall odds of HPV vaccination completion by 92%. We theorize that the decision prompts remind health care providers to discuss or recommend the HPV vaccination during clinical service delivery. CDPs in the EMR help increase HPV vaccination rates in multiple specialties and are a low-cost intervention for improving 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 | 0.017 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".