Description: Healthcare risk adjustment methodologies have become increasingly complex, largely due to the belief that complexity enhances accuracy. However, a disconnect exists between how healthcare risk is assessed and how payments are adjusted. This article explores the stark difference between measuring the accuracy of a risk assessment model at a member level, and that of a risk adjustment mechanism at the health plan level. I show that a highly simplified approach that does not use diagnosis codes can have similar accuracy on risk adjusted payments to models that do. These findings suggest that accuracy is not a barrier to developing simpler risk adjustment methodologies, which can improve payment accuracy and reduce the financial incentive for coding diagnoses.

Description: The presenters will provide insights into fraud and abuse risk in the health insurance industry, including an examination of traditional and predictive modeling techniques for both prevention and detection. Emphasis will be placed on current and emerging risks. Canadian examples and perspectives will be offered, but the applications for attendees to use in practice are non-nation specific.