The economic impact of the pharmacist in heart failure ambulatory care clinics: A scoping review
Bibliographic record
Abstract
Abstract Introduction Heart failure (HF) is a chronic condition and a leading cause of hospitalization with high rates of mortality. HF is associated with high costs related to hospitalization, medical procedures, treatments, and medications, and is projected to reach $69.8 billion by 2030 in the United States. As pharmacists are being integrated into HF clinics, studies have shown that clinical pharmacy interventions reduce the number of hospitalizations and deaths. While clinical benefits are clear, the economic impact of different pharmacist interventions remains to be proven. Methods We aimed to conduct a scoping review to narratively synthesize the literature describing the costs of clinical pharmacy interventions in ambulatory HF clinics. MEDLINE, Embase, and CINAHL were searched from database inception to May 2024. We searched for articles containing all four key concepts: ambulatory clinic, pharmacist, heart failure, and cost. Results From 189 records identified, we included 10 studies published in English. The presence of a pharmacist in HF clinics or programs showed an overall trend of reduction in costs related to hospitalization and overall health care costs. Additionally, the overview of the literature and methodology of other studies allowed us to identify the variables necessary to establish an economic model. These include the number of patients seen by the pharmacist, type and number of interventions performed, number and costs of hospitalizations, number and costs of health care appointments or visits in and out of the HF clinic, costs of drugs taken per patient, pharmacists' salary, full‐time equivalent of pharmacists, and uptake of guideline‐directed medical therapy (GDMT). Conclusion Based on this scoping review, clinical pharmacy intervention has demonstrated cost reduction, but has not yet been formally evaluated using a cost‐effectiveness or utility design. This review provides the framework required for a future economic study.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".