Using Self-Reported Health Measures to Predict High-Need Cases among Medicaid-Eligible Adults

Medicaid agencies are increasingly adopting care coordination and management strategies for their high-need beneficiaries, often with the twin goals of enhancing quality of care and reducing costs (Verdier, Byrd, and Stone 2009; Kim et al. 2013). This trend is likely to accelerate as states implement the Affordable Care Act's (ACA) new options to coordinate the care of chronically ill Medicaid beneficiaries (Kaiser Family Foundation 2011) and, in at least 28 states, expand Medicaid coverage to a larger population (Advisory Board 2013). To target high-need individuals, care management initiatives typically apply a case-finding approach that uses predictive models to stratify patients along likely future health care utilization (Knutson, Bella, and Llanos 2009; Verdier, Byrd, and Stone 2009). There is a mismatch, however, between the data requirements of these models, which rely on historical medical claims, and the data available to Medicaid programs for both new and “churning” beneficiaries, of whom states expect increasing numbers under the ACA (Knutson, Bella, and Llanos 2009). This article proposes and tests a practical alternative to a claims-based algorithm for identifying high-need Medicaid beneficiaries, one that relies upon well-validated self-reported health (SRH) measures that states or managed care organizations (MCOs) may potentially collect during the initial application and enrollment process.