Effect of a Pharmacist‐Managed Hypertension Program on Health System Costs: An Evaluation of the Study of Cardiovascular Risk Intervention by Pharmacists—Hypertension (<scp>SCRIP</scp>‐<i><scp>HTN</scp></i>)
Bibliographic record
Abstract
STUDY OBJECTIVE: To quantify the potential cost savings of a community pharmacy-based hypertension management program based on the results of the Study of Cardiovascular Risk Intervention by Pharmacists-Hypertension (SCRIP-HTN) study in terms of avoided cardiovascular events-myocardial infarction, stroke, and heart failure hospitalization, and to compare these cost savings with the cost of the pharmacist intervention program. DESIGN: An economic model was developed to estimate the potential cost avoidance in direct health care resources from reduced cardiovascular events over a 1-year period. MEASUREMENTS AND MAIN RESULTS: The SCRIP-HTN study found that patients with diabetes mellitus and hypertension who were receiving the pharmacist intervention had a greater mean reduction in systolic blood pressure of 5.6 mm Hg than patients receiving usual care. For our model, published meta-analysis data were used to compute cardiovascular event absolute risk reductions associated with a 5.6-mm Hg reduction in systolic blood pressure over 6 months. Costs/event were obtained from administrative data, and probabilistic sensitivity analyses were performed to assess the robustness of the results. Two program scenarios were evaluated-one with monthly follow-up for a total of 1 year with sustained blood pressure reduction, and the other in which pharmacist care ended after the 6-month program but the effects on systolic blood pressure diminished over time. The cost saving results from the economic model were then compared with the costs of the program. Annual estimated cost savings (in 2011 Canadian dollars) from avoided cardiovascular events were $265/patient (95% confidence interval [CI] $63-467) if the program lasted 1 year or $221/patient (95%CI $72-371) if pharmacist care ceased after 6 months with an assumed loss of effect afterward. Estimated pharmacist costs were $90/patient for 6 months or $150/patient for 1 year, suggesting that pharmacist-managed programs are cost saving, with the annual net total cost savings/patient estimated to be $131 for a program lasting 6 months or $115 for a program lasting 1 year. CONCLUSION: Our model found that community pharmacist interventions capable of reducing systolic blood pressure by 5.6 mm Hg within 6 months are cost saving and result in improved patient outcomes. Wider adoption of pharmacist-managed hypertension care for patients with diabetes and hypertension is encouraged.
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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 | 0.018 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".