Abstract FR477: Implementing the CDC’s Hypertension Management Program Toolkit in a Federally Qualified Health Center (FQHC)
Bibliographic record
Abstract
Introduction: The CDC’s HTN Management Program (HMP) Toolkit provides strategies to improve HTN control rates. The study implemented components of the HMP Toolkit in a FQHC to examine the impact on HTN control. Hypothesis: Does implementing components of the HMP Toolkit over 12 weeks improve BP measurement technique, BP rechecks, SPC prescribing, and HTN control? Methods: The study took place in 2025 at a FQHC in Rochester, NY. A quantitative descriptive design was utilized and data analysis included descriptive statistics and Repeat Measures Analysis of Variance (ANOVA) test. Components implemented: a) Patient registry/outreach Inclusion criteria (IC): 1) patients 18-85yo with a known diagnosis of HTN 2) on ≤ 2 antihypertensive medications 3) previous two BP readings ≥ 140/90 4) last visit was <12 months ago and have no visit scheduled in the next 3 months Outcomes measured: HTN Management Visit (HMV) with PharmD b) EHR Alert for repeat BP check IC: all patients seen by providers Outcomes measured: repeat BP obtained when initial BP ≥140/90 c) Education for nurses on BP measurement technique IC: all staff (MA, LPN, RN) who room patients Outcomes measured: Quick Check Assessment tool from AHA performed pre- and post-educational intervention d) Promote use of SPC to treat HTN IC: all primary care providers and PharmDs Outcomes measured: prescriptions for SPC therapy e) PharmD-led HMVs IC: patients from HTN Registry/Referrals who participated in the HMV with PharmD Outcomes measured: SBP measured pre- and post-HMV Results: a) EHR alerts for repeat BP check - Appropriate repeat BP measurement increased from 67% to > 90% (n= 670, 606 repeats performed). b) Education for nurses on BP technique - Baseline Quick Checks (n=55 observations) showed common errors and most domains improved on repeat observation (n = 45). c) Promote use of SPC - SPC prescribing doubled from baseline of 12% to 24% (n = 544 SPC prescribed). d) PharmD-led HMV - Average SBP change from the patients’ first HMV to their second HMV was -17 mm Hg with a strong statistical significance noted (p <.001). In terms of HTN control, 39% (n=10/26) reached BP ≤130/80 and 73% (n=19/26) reached BP ≤140/90. Conclusion: Implementing select components from the HMP Toolkit resulted in improved repeat BP rates, improved BP measurement technique, and increased SPC prescribing rates. Additionally, patients who participated in the PharmD-led HMVs saw an average 17 mm Hg reduction in SBP with 73% of these patients reaching target BP of ≤140/90.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".