Reimagining kidney value-based care: leveraging data science for dynamic, clinician-level risk prediction

Chronic kidney disease (CKD) remains one of the costliest conditions in healthcare, yet value-based payment (VBP) reforms have produced limited improvements in cost or quality. Existing renal payment models rely on annual benchmarks that fail to reflect the longitudinal reality of CKD care. This precludes the possibility of observing a dose-response relationship in clinician-level interventions, effectively invisible in current VBP structures.

Advances in clinical informatics and predictive modeling demonstrate the feasibility of sub-annual, patient-level risk estimation for CKD outcomes and costs. Standardized electronic health record and claims data enable longitudinal, clinician-level analysis. This can transform retrospective, aggregate VBP into dynamic forecasting systems reflecting real-world care delivery. Short-horizon prediction reveals temporal, dose-response relationships between guideline-aligned interventions and downstream events that remain obscured by annualized VBP frameworks.

CKD is a clinically and economically compelling testbed for next-generation VBP design. Patient-level modeling with sub-annual risk prediction is the next step in modernizing payment frameworks to align incentives. Future VBP policy should shift models beyond static, annual metrics toward EHR-native, temporally precise evaluation frameworks that reward meaningful preventive care for patients, clinicians, and payers.