Quality Improvement Strategies for Hypertension Management
Bibliographic record
Abstract
BACKGROUND: Care remains suboptimal for many patients with hypertension. PURPOSE: The purpose of this study was to assess the effectiveness of quality improvement (QI) strategies in lowering blood pressure. DATA SOURCES: MEDLINE, Cochrane databases, and article bibliographies were searched for this study. STUDY SELECTION: Trials, controlled before-after studies, and interrupted time series evaluating QI interventions targeting hypertension control and reporting blood pressure outcomes were studied. DATA EXTRACTION: Two reviewers abstracted data and classified QI strategies into categories: provider education, provider reminders, facilitated relay of clinical information, patient education, self-management, patient reminders, audit and feedback, team change, or financial incentives were extracted. DATA SYNTHESIS: Forty-four articles reporting 57 comparisons underwent quantitative analysis. Patients in the intervention groups experienced median reductions in systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were 4.5 mm Hg (interquartile range [IQR]: 1.5 to 11.0) and 2.1 mm Hg (IQR: -0.2 to 5.0) greater than observed for control patients. Median increases in the percentage of individuals achieving target goals for SBP and DBP were 16.2% (IQR: 10.3 to 32.2) and 6.0% (IQR: 1.5 to 17.5). Interventions that included team change as a QI strategy were associated with the largest reductions in blood pressure outcomes. All team change studies included assignment of some responsibilities to a health professional other than the patient's physician. LIMITATIONS: Not all QI strategies have been assessed equally, which limits the power to compare differences in effects between strategies. CONCLUSION: QI strategies are associated with improved hypertension control. A focus on hypertension by someone in addition to the patient's physician was associated with substantial improvement. Future research should examine the contributions of individual QI strategies and their relative costs.
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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 | 0.055 | 0.131 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| 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".