This session explores application of risk adjustment and predictive modeling through brief case studies involving key topics; examines the potential of enhanced models to identify patients with rising risk; and considers the impact and implications of analyzing prescription data to determine future patient costs and serve as predicators regarding opioid abuse patients.

Sessions will include: Population Health Management: Innovations in Risk Adjustment and Predictive Modeling; Risk Adjustment and Shared Savings Agreements; and Connecting Predictive Modeling and End-Users: the Last Mile Problem.

Sessions include: Predictive Modeling Opportunities, Issues and Implications from Richer Data Streams via EHR and Other Sources; Medication Adherence Interventions: using predictive modeling and risk stratification to target and improve program efficiency; Protons Don't Smoke - A unified theory for biologic science - in the context of big data in healthcare.