Association between pharmacist-led telehealth services and improvements in cardiovascular outcomes among patients with cardiovascular risk factors: A scoping review
Bibliographic record
Abstract
Background: Cardiovascular disease is the leading cause of death globally. Despite the effectiveness of lifestyle changes and recommended therapeutics, access to primary care and treatments to improve cardiovascular risk-factors (CRFs) remains challenging. Pharmacists and telehealth services have been proposed as potential solutions to overcome these barriers. Methods: PubMed, OVID, and CINAHL databases were searched from January 2006 to March 2023. The primary outcomes were changes from baseline in systolic/diastolic blood pressure, glycated hemoglobin (A1c), cholesterol levels, and adherence to any patient counseling. Only studies conducted in the United States and Canada were included in the review. Results: Of 110 screened bibliographic records, 14 studies were included in the review. The pharmacist-led telehealth interventions included medication therapy management, medication reviews, and counseling on lifestyle changes. Nine studies reported significant improvements with intervention, 7 studies on CRFs and 2 studies on medication adherence at the 12-month follow-up, when pharmacist-led telehealth services were compared to usual care or historical data (p < 0.05). Conclusion: This scoping review provides evidence for continued support to the development and implementation of pharmacist-led telehealth services in primary cardiovascular care. The findings suggest that pharmacist-led telehealth interventions can improve cardiovascular outcomes and adherence to drug and non-drug therapy among patients with CRFs. However, because of lack of published randomized clinical studies on patients with CRFs residing in underserved communities, future directions in research should focus on exploring the implementation of pharmacist-led telehealth services in rural or underserved communities, utilizing various payment models to enhance accessibility and feasibility.
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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 | 0.009 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".