Artificial intelligence and predictive analytics are being scaled to segment patients into several different areas, including risk for CKD progression and eventual need for dialysis. These models are used to develop effective and efficient integrated care delivery systems. Incorporating this technology into care delivery will be essential to helping reduce CKD rates and slow disease progression.

There is a shift to move all sectors of health care away from traditional fee-for-service models that pay clinicians based on the number of patients seen or procedures performed to value-based care.

Each year, approximately 100,000 patients transition from chronic kidney disease (CKD) to end-stage kidney disease (ESKD).