Ensuring Better Kidney Care

Advanced Algorithms Drive Patient Identification and Care Management

Ensuring Better Kidney Care
BRIEFING
November 12, 2020

Chronic kidney disease (CKD) affects more than one in seven American adults and accounts for a substantial portion of our nation’s health care costs.Centers for Disease Control and Prevention. Chronic Kidney Disease in the United States, 2019. Atlanta, GA: US Department of Health and Human Services, Centers for Disease Control and Prevention; 2019.United States Renal Data System. 2019 USRDS annual data report: Epidemiology of kidney disease in the United States. National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, MD, 2019.

In many cases, CKD progresses silently over many years, with some individuals not knowing that they have a problem. People often show no symptoms until they reach the final stage of the disease, known as kidney failure or end-stage renal disease. Once someone’s kidneys fail, they need a transplant or dialysis. Either treatment option is expensive.https://www.ajmc.com/view/all-cause-costs-increase-exponentially-with-increased-chronic-kidney-disease-stage-article.

Going Beyond the Disease Stage

CVS Kidney Care harnesses comprehensive analytics developed from several datasets — medical, pharmacy, and lab data — and applies advanced machine learning algorithms to identify plan members with the highest risk of starting dialysis over the next three years.

Compared to stratifying by stage alone, our models can identify 105 percent more members expected to progress to kidney replacement therapy.* This means we can include more high-risk members in engagement efforts to reduce overall health care costs related to kidney disease.

Analytics in Action

Knowing when and how to engage members enables us to better predict how quickly someone is progressing toward needing kidney replacement therapy (for example, transplant or dialysis).**

The models are able to estimate the speed of kidney disease progression — who might be years away from kidney failure, or who might need kidney replacement therapy within the next few months. Greater accuracy in predicting planned start or time to dialysis can help us tailor engagement and outreach strategies aimed at improving outcomes and quality of life.

Predictive Modeling to Transform Care Management

Our risk prediction models can transform care for kidney disease by tailoring support to members’ needs. After identifying members at risk for disease progression and how quickly their disease may advance, we provide targeted care management to help delay the progression.

If a member is identified to be at high risk of advancing toward kidney replacement therapy, trained educators provide appropriate engagements and promote behavior helpful in preserving kidney function for as long as possible to help delay the onset of end-stage disease and the need for dialysis. Targeted care management can help prevent hospitalizations and re-hospitalizations, an estimated $20,000 savings per avoided admission.

CVS Kidney Care is dedicated to improving quality of life for people with kidney disease throughout the disease continuum. By engaging people at the right time and with the right support, we can better prepare them to make informed decisions about treatment to help improve their quality of life and clinical outcomes.

For more information, contact CVS Kidney Care or reach out to your CVS Caremark Account Representative.

BRIEFING
November 12, 2020

*Analysis of only the CKD stages 4 and 5.

**CVS Kidney Care analytics, 2020. Latest model performance metrics from CVS Kidney Care Analytics team. All data sharing complies with applicable law, our information firewall and any applicable contractual limitations. Actual results may vary depending on benefit plan design, member demographics, programs implemented by the plan and other factors.

Must have a minimum of 5,000 individually insured lives to qualify for the services described.

Data source, unless noted otherwise, CVS Health Enterprise Analytics, 2020.

Image source: Licensed from Getty Images, 2020.