Drivers and scorecards to improve hypertension control in primary care practice: Recommendations from the HEARTS in the Americas Innovation Group
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of morbidity and mortality in the Americas, and hypertension is the most significant modifiable risk factor. However, hypertension control rates remain low, and CVD mortality is stagnant or rising after decades of continuing reduction. In 2016, the World Health Organization (WHO) launched the HEARTS technical package to improve hypertension control. The Pan American Health Organization (PAHO) designed the HEARTS in the Americas Initiative to improve CVD risk management, emphasizing hypertension control, to date implemented in 21 countries. Methods: To advance implementation, an interdisciplinary group of practitioners was engaged to select the key evidence-based drivers of hypertension control and to design a comprehensive scorecard to monitor their implementation at primary care health facilities (PHC). The group studied high-performing health systems that achieve high hypertension control through quality improvement programs focusing on specific process measures, with regular feedback to providers at health facilities. Findings: The final selected eight drivers were categorized into five main domains: (1) diagnosis (blood pressure measurement accuracy and CVD risk evaluation); (2) treatment (standardized treatment protocol and treatment intensification); (3) continuity of care and follow-up; (4) delivery system (team-based care, medication refill), and (5) system for performance evaluation. The drivers and recommendations were then translated into process measures, resulting in two interconnected scorecards integrated into the HEARTS in the Americas monitoring and evaluation system. Interpretation: Focus on these key hypertension drivers and resulting scorecards, will guide the quality improvement process to achieve population control goals at the participating health centers in HEARTS implementing countries. Funding: No funding to declare.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".