Medicare's rapid move to value-based pay has pushed population health management to the top of the priority list for health systems and ACOs.
CKD, a common, costly comorbidity in diabetes and heart failure, is currently managed in a suboptimal manner, as providers lack tools to accurately predict which patients will progress to renal failure requiring dialysis or transplant, and the CKD patient population is heterogeneous. A new, automated, globally validated solution for predicting the risk of progression in CKD patients shows promise for improving patient outcomes and organization profitability.
Medicare's rapid move to value-based pay necessitates the use of population health management (PHM) in Accountable Care Organizations (ACOs) and health systems. Among the top diseases presenting the biggest challenges in PHM are diabetes and congestive heart failure (CHF), given their high prevalence, expensive interventions, and associated complications.
While the payer mandate is clear — get costs under control and improve patient outcomes — the way forward is murkier. In the quagmires of diabetes and CHF population health management, comorbidities can complicate resource focus in order to pull free of the costly status quo.
CKD's role as a comorbidity in diabetes and CHF
While several complications and comorbidities demand attention in successfully managing diabetic and CHF healthcare, numerous studies indicate that chronic kidney disease (CKD) is high on the list of top cost drivers.
CKD affects just under half of the Medicare and diabetic population.1,2 And, for the 65 and older subgroup with type 2 diabetes, the prevalence of CKD is 61 percent.3
The high prevalence of renal disease in diabetics results in staggering healthcare costs. Indeed, CKD is the only comorbidity that predicts which diabetics are in the top highest percentile of costs. The top 10% of diabetics had $58,000 greater annual costs than the lower 90% of diabetics.4,5
CKD is a top cost driver in managing other prevalent and costly chronic conditions as well, such as congestive heart failure (CHF). More than 40% of patients with CHF also have CKD.6
Given CKD's role as a comorbidity in multiple diseases, it makes sense to focus on proactively and precisely managing it since this represents a clear path to high-impact disease management cost reductions.
The benefits in sharpening the focus on CKD
While the opportunities to reduce costs dramatically through improved CKD management and predictions are clear in concept, they are not readily evident in practice. Traditional staging based on glomerular filtration rate (GFR) is not predictive in terms of individual disease progression. As a result, some patients are over-treated, effectively wasting limited resources and negatively impacting outcomes. Others receive inadequate treatment, restricting options to only the most costly and least desirable interventions.
If patients' risk of progressing could be predicted, then providers would be armed with much more significant clinical decision support, patients would receive better care, and ACOs and health systems would be better positioned to hit the Triple Aim: better resource allocation, targeted care, and improved patient outcomes.
The question then becomes how to accurately predict individual progression up the staging chain. The answer is found in sophisticated analytics capable of predicting disease progression at the individual patient level.
The Kidney Failure Risk Equation (KFRE) breakthrough
One such breakthrough is Dr. Navdeep Tangri's development of the Kidney Failure Risk Equation (KFRE), a globally validated algorithm based on patient laboratory test and demographic data, which predicts with greater than 90% accuracy whether a specific patient's CKD will progress to ESRD.7
The output is a single patient risk score representing the probability of end stage renal disease at two, four and five years, combined with care protocol recommendations. This information can be automated and implemented into providers' workflow with actionable protocol, including:
• PCP referral to nephrologist – seeing a nephrologist before ESRD has been shown to improve outcomes and decrease costs
• Nephrologist discuss dialysis options – pre-dialysis education is important to ensure successful ESRD treatment. Options like home dialysis provide higher value for patient and health system savings.
Providers can share these results with their patients in personalized reports containing their risk of renal failure and care recommendations.
The advantage of CKD patient risk scores to ACOs and health systems is the accurate read on which patients require advanced and aggressive treatments, and which require more routine maintenance regimens, and allocate resources accordingly. Patients are spared harsher treatments and enjoy better outcomes while organizations keep costs down — all because of a well-designed and accurate algorithm.
Indeed, estimates on cost savings to health organizations using these particular analytics are roughly $2.8 million in savings for an adult population of 25,000 covered lives, and $8.4 million for a Medicare population of 25,000 covered lives.8
In summary, managing diabetes and CHF populations in line with value-based payer requirements calls for targeting those factors that drive high costs and are amenable to improvement. With the advent of a new predictive analytics tool, CKD is now this opportunity.
It requires accurate and individualized predictive capabilities to identify which patients need more aggressive care, where waste exists, and where resources should be allocated.
In this age of precision medicine, personalized health assessments and predictions allow ACOs and health systems to provide better care, improve outcomes, and increase patient satisfaction, while realizing substantial cost savings.
Eleanor Herriman, MD, MBA is a physician executive with 20 years of varied healthcare industry experience. As the Chief Medical Informatics Officer at Viewics, she is critical in shaping product strategy for the healthcare-focused insights company, which provides hospitals and health systems with solutions for advanced analytics, including clinical decision support, predictive analytics, population health management, and personalized medicine.
Dr. Herriman is a former Senior Fellow with Professor Michael Porter at Harvard Business School's Institute for Strategy and Competitiveness. Prior experience includes: market research and strategy services to the pathology and laboratory industries at G2 Intelligence, healthcare strategy consulting at Bain & Company, and multiple medical technology startup ventures.
Dr. Herriman holds a Doctor of Medicine Degree from Baylor College of Medicine, a Masters in Business Administration from Harvard University Graduate School of Business Administration as a Baker Scholar. Her pathology residency was completed at the University of California, San Francisco. She earned a Bachelors of Science in electrical engineering with a minor in bioengineering from Rice University, Magna Cum Laude.
1 Braun LA et al. High burden and unmet patient needs in chronic kidney disease. Int J of Neph and Renovasc Disease 2012:5 151–163.
2 Hoerger TJ et al. The Future Burden of CKD in the United States: A Simulation
Model for the CDC CKD Initiative. Am J Kidney Dis. 2015;65(3):403-411.
3 Bailey RA, Wang Y, Zhu V, Rupnow MF. Chronic kidney disease in US adults with type 2 diabetes: an updated national estimate of prevalence based on Kidney Disease: Improving Global Outcomes (KDIGO) staging. BMC Research Notes. 2014;7:415. doi:10.1186/1756-0500-7-415.
4 Shiba, Nobuyuki et al. Chronic kidney disease and heart failure—Bidirectional close link and common therapeutic goal. Journal of Cardiology. Vol. 57, Issue 1 , 8 – 17
5 Meyers et al. The high-cost, type 2 diabetes mellitus patient: an analysis of managed care administrative data. Archives of Public Health 2014, 72:6.
6 Li, R. et al. Medical Costs Associated With Type 2 Diabetes Complications and Comorbidities. Am J Managed Care 2013 May; 19(5): 421-430
7 Tangri, N. et al. Multinational Assessment of Accuracy of Equations for Predicting Risk of Kidney Failure. JAMA. 2016;315(2):164-174.
8 Data on file from Viewics. Data modeling based on research from Tangri N et al. JAMA. 2016;315(2):164-174; Personal communication with Navdeep Tangri, MD, FRCP(C); Lee et al. BMC Health Services Research 2012, 12:252; Lee J, et al. (2014) PLoS ONE 9(6): e99460; Reaven NL et al. Am J Pharm Benefits. 2014;6(6):e169-e176.
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