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Five percent of the U.S. population has complex medical and behavioral or social needs, but this group accounts for 50 percent of the country’s health care spending. New payment and care delivery models such as accountable care organizations (ACOs) have prompted decision-makers at health care delivery systems to seek the best ways to meet these patients’ needs while controlling costs.
To this end, many ACOs have used predictive modeling and risk stratification to sort their entire population into risk levels (such as low, medium, and high). ACOs typically linked their high-risk patients to the ACO’s general care management program. This approach has had mixed results, perhaps because high-risk patients have wide-ranging, heterogeneous needs, and different care management services benefit certain kinds of high-risk patients more than others.
Fewer ACOs have taken the approach of subdividing (segmenting) this high-need, high-cost (HNHC) population into smaller subgroups with similar needs. The National Academy of Medicine and others have highlighted the importance of recognizing that all HNHC patients are not alike, and recommend segmentation of HNHC patients. It is theorized that segmentation will allow ACOs to better match patients to appropriate interventions, enabling them to provide higher-quality care and allocate limited resources more effectively. Interventions are most effective when they target the patients that they were intended to serve.For example, an intervention might include outreach to socially isolated patients with congestive heart failure (CHF); additional social support might improve their medical condition and avoid preventable emergency department (ED) visits.