Third-party Reimbursement of Pharmacist-Led Cardiovascular and Diabetes Preventive Health Services for Workplace Health Initiatives: A Narrative Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To summarize available literature describing third-party payer reimbursement models for pharmacist-led preventive health services as part of workplace health initiatives. METHODS: A combination of search terms related to pharmacists, preventive health, and third-party reimbursement were searched in MEDLINE, EMBASE, and PubMed. Included studies described community pharmacist-led cardiovascular and diabetes preventive health service to employees older than 18 years of age as part of a workplace health program with corresponding third-party reimbursement models. Programs that were reimbursed by government resources or studies lacking reimbursement model details were excluded. One reviewer performed level 1 screening and three reviewers analyzed included studies. RESULTS: The search criteria yielded 863 results. Sixteen articles were reviewed after level 1 screening and 13 were ineligible and excluded. Three studies with varying quality of reporting were included. Reimbursement models varied from $40 USD for a 20-minute visit to $391 to $552 USD total per patient with an average of 6 visits per patient. CONCLUSION: There is a lack of quality literature describing third-party reimbursement models for pharmacist-led preventive health services, which hinders the ability to implement a standardized model. High quality studies evaluating the cost of reimbursing pharmacist-led cardiovascular preventive health services compared to the savings to the third-party payer should be performed to inform the standardization of payment models.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.010 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".