Nephrology practice and research network opinion paper: Pharmacists' perspectives on the Advancing American Kidney Health initiative
Bibliographic record
Abstract
Abstract Patients with chronic kidney disease (CKD) are at high risk for clinically significant medication therapy problems (MTPs) due primarily to the complex pharmacotherapy prescribed by numerous providers, and the significant changes in pharmacokinetic and pharmacodynamic properties of many medications due to their kidney disease. While MTPs are well recognized, pharmacists are not routinely involved in the health care teams that provide care for patients with CKD. The Advancing American Kidney Health initiative was announced in July 2019 and is poised to shift models of care in nephrology toward value‐based payment. This represents an opportunity for pharmacists to integrate into teams to improve quality of care and outcomes for patients with CKD. Pharmacists should engage in medication reconciliation, transitions of care, and medication‐focused kidney disease education programs within a comprehensive medication management (CMM) framework to optimize pharmacotherapy outcomes. To implement these services, pharmacists need to utilize certified pharmacy technicians, telehealth, interprofessional skills, and other tools in innovative ways. Current models of successful practice from Canada can serve as a template for United States‐based nephrology pharmacy practice. In this opinion paper, we characterize the current state of nephrology practice and identify significant opportunities for advancement. We propose metrics to evaluate pharmacy services and a framework for key stakeholder engagement which will be critical to advance the profession of pharmacy within nephrology. The Advancing American Kidney Health initiative offers a distinct opportunity to demonstrate the value of CMM provided by pharmacists in the nephrology arena to optimize patient care.
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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 | 0.058 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.020 | 0.019 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".