In partnership with UCLA’s Faculty Practice Group (FPG), we are looking to better identify individuals within our healthcare enterprise who are at risk for worsening kidney function. Chronic kidney disease (CKD) affects a significant number of Americans, and if left untreated results in the need for dialysis and/or kidney transplant. Proper clinical management of individuals early in the course of the disease can sometimes recover impaired kidney function, if not slow or stabilize disease progression. Unfortunately, a number of patients suffer from rapid decline of kidney function, and it is presently unclear who these individuals will be. As part of the UCLA CTSI’s Clinical Pathways program, we are using machine learning (ML) methods on EHR-derived data to better stratify UCLA patients by identifying early patterns indicative of CKD patients who may be at risk of rapid decline. By finding these patterns in a timely manner, more targeted preventive interventions can be put in place to help the patient and prevent disease progression.