The Impact of Interdisciplinary Care Teams in Value-Based Kidney Care: Insights from Case Study Reports
Bibliographic record
Abstract
Value-based care (VBC) aims to improve patient health outcomes relative to the cost of care by shifting from traditional fee-for-service models to patient-centered, outcome-driven approaches. This framework is particularly important in nephrology, where high costs associated with chronic kidney disease (CKD) and end stage kidney disease have prompted the adoption of new care models. Key programs such as the Comprehensive End-Stage Renal Disease Care Model and the Kidney Care Choices program have introduced multidisciplinary teams and early-stage CKD interventions to improve patient outcomes and reduce costs. This article highlights the essential role of interdisciplinary collaboration in VBC, with registered nurses, nurse practitioners, pharmacists, social workers, dietitians, and physicians coordinating care to address clinical and non-clinical needs. Case studies demonstrate the effectiveness of coordinated efforts in medication management, patient education, and addressing social determinants of health. These examples underscore the potential for VBC to significantly improve patient outcomes in kidney care while addressing health care inequities and reducing overall costs. Findings emphasize the importance of early interventions, interdisciplinary teamwork, and targeted support for patients with CKD in achieving VBC outcomes.
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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 | 0.012 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| 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".