The new payment model Kidney Care Choices offer benefits enhancements that nephrology practices have not explored in depth. Let's explore useful benefits outlined in the current model.
Urine luck! We've got a lot going on this week down in DC and would love for you to join us. A talk, poster sessions and a happy hour! Get in on the fun.
Risk prediction of end stage renal disease (ESRD) for population management and care intervention is both a research priority and unmet public health need. The use of electronic medical records (EMR) can be leveraged for improved assessment of ESRD onset. However, traditional risk scoring may not provide accurate risk prediction or complete population coverage if EMR data is incomplete. To handle missing data we developed a machine learning (ML) approach and compared it to traditional risk scoring in two EMR cohorts.
Chronic Kidney Disease (CKD) is an under-identified condition and current methodology for identifying patients at risk of developing incident CKD is limited. Identifying patients who are high risk for CKD can improve awareness while delaying onset and progression of CKD. Machine learning algorithms can be used to stratify risk of those likely to develop incident CKD. Previous work has defined CKD using ICD codes or a limited number of eGFR readings.
Building on our previous work using machine learning techniques to identify patients at risk of progression to End Stage Renal Disease (ESRD), we focused this model more precisely on identifying patients with advanced kidney function loss who should plan for optimal renal replacement therapy. The most recent USRDS data indicates that more than 80% of patients begin hemodialysis with a catheter and only 2.5% receive preemptive renal transplants.
This case study describes The Rogosin Institute’s (Rogosin’s) experience in
implementing a health literacy initiative focused on chronic kidney disease (CKD).
