Maximizing Care Management Savings through Advanced Total Population Targeting
Published in Outcomes and Insights in Health Management
Author(s): Elizabeth Rula, PhD; Adam Hobgood, MS; Karen S. Hamlet, MA; Huiwen Zeng, MA; and Michael F. Montijo, MD, MPH, FACP
The increasing burden of chronic disease in the United States and other industrialized nations continues to drive healthcare costs to new heights. To reverse current trends, organizations are employing care management programs that target individuals with chronic conditions. The goals is to reduce costs and morbidity through better condition management. The challenge is to identify individuals for whom intervention can make a difference and find intervention opportunities at the right time: before adverse health outcomes escalate medical costs.
The dynamics of medical spending within a population can be counter intuitive. Spending is not solely driven by specific diagnoses, diseases, and clinical risks, nor by the same individuals from year to year. The potential for reducing costs varies among high-cost individuals. To effectively identify the appropriate individuals for an intervention, predictive models must take these dynamics into account.
Five principal rules govern healthcare costs within a total population and should guide the targeting of care management interventions. This document reviews these rules and outlines how Healthways’ predictive modeling strategies segment the population for the most effective targeting of interventions and the greatest impact on healthcare expenditures.
- Five percent of the population is responsible for more than half of spending.
- High-cost individuals vary by year; chronic disease and health risks are insufficient predictors
of short-term costs.
- Not all cost is avoidable; greater value is derived by identifying individuals with mitigable risk.
- Sharecare predictive models are effective at identifying the members of a population with
near-term, avoidable spending increases, providing the opportunity to intervene prior to cost escalation.