Stratifying for Value: An Updated Population Health Risk Stratification Approach.

Abstract

Most risk stratification approaches attempt to predict clinical outcomes rather than value. For a provider organization or health system to have financial success in value-based contracting, future risk models must analyze costs as well as disease burden. The purpose of this study was to create a customized risk stratification algorithm that considered a patient's medical spend alongside disease burden while delivering a scoring system that improves the efficiency of a care coordination program. The authors focused on University Hospitals (UH) Health System's Accountable Care Organization population of 554,805 because this patient cohort is engaged with UH's primary care network and has the most robust data. The 5-category risk algorithm was found to be meaningful and impactful after integrating the foundation of the Minnesota Tiering system with an expanded comorbidity list and weighting the result by the previous 12 months of medical spend. This new technique can identify patients in need of intensive care coordination. The complex risk tier of the stratification system reduces the number of patients from 551,045 to 27,552, or 5% of the patient population, and accounts for 67.9% ($1,107,822,887) of total annual medical spend. Expanding care coordination efforts to patients in the top 2 tiers would account for 15% of the patients and 83.2% ($1,357,545,872) of annual medical spend. The novelty of the new approach allows clinical teams to focus intense resources on a smaller sample of the patient population and to identify chronic conditions contributing to costs, and feel confident that they have greater explanatory power regarding value.