Population Health Management Approach: Integration of Community-Based Pharmacists into Integrated Care Systems: Reflections from the U.S., Achievements in Scotland and Discussions in Germany
Bibliographic record
Abstract
The annual amount spent on healthcare per capita is higher and expected to grow in the U.S. compared to healthier level 4 countries (e.g., United Kingdom, Canada, Germany, Australia, Japan, Sweden, Netherlands), while health outcomes continue to be suboptimal [1, 2, 3]. Therefore, healthcare is slowly shifting from a fee-for-service to value-based care, which addresses social determinants of health, promotes outcome-based contracting and employs more Population Health Management (PHM) activities. The root cause for this shift has been the increase in patients’ out-of-pocket costs and the pervasiveness of poorer outcomes. PHM has been defined by many as a mindset and activities that support the Triple Aim Initiative (i.e., improving population health, experience of care, reducing costs) [4].This article outlines the value of pharmacists on health outcomes in the U.S., Germany, and Scotland and innovative PHM approaches through pharmacist collaborative networks, polypharmacy management and pharmacists’ integration in care models [1, 5].
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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 | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".