The Centers for Medicare and Medicaid Services (CMS) is adding a new prescription drug category classification system to the 2018 risk adjustment model. Starting in 2018, a condition will be identified through a Hierarchical Condition Category with associated medical diagnosis codes, a prescribed medication, or both—each one affecting the final risk member score differently. This paper by Milliman consultants approximates the likely CMS mapping based on the publicly available information to date.
Effective management of information entered into an External Data Gathering Environment (EDGE) server may save health plans millions of dollars in risk adjustment transfer payments. In this paper, Milliman’s Jason Petroske and Alan Vandagriff outline best practices that issuers should consider as part of their annual EDGE server submission cycles to maximize risk adjustment results.
Here’s an excerpt:
Complete and accurate data is a critical element in capturing—and, more importantly, in receiving compensation for—a health plan’s true level of risk. While navigating the first two years of EDGE submissions, we have mapped out a comprehensive action plan focused on three main areas that any issuer can integrate into its data management framework:
• Establish a robust review and reconciliation process: Create a continuous process for reviewing and reconciling EDGE submissions to internal data sources. Identify key metrics for data completeness and use the test environment to ensure each EDGE submission passes these standards before finalizing in production.
• Prioritize error corrections: Not all errors are created equal, so have a strategic plan for correcting errors and improving data quality. Understand the economics of risk adjustment to help effectively deploy and allocate resources.
• Track data quality and establish benchmarks: Track and benchmark data quality and submission results over time. Look for patterns in errors or outliers from prior submissions as these can be signals of systemic weaknesses in the overall data management process.
Risk adjustment may influence insurers’ profitability in the health insurance marketplace, and the volatility of profit results may be highly linked to insurers’ plan size. In this analysis, Milliman consultants examine how risk adjustment might influence profitability patterns and whether those patterns change with the size of a health plan. The authors also address main concepts behind two sets of proposals that have emerged to improve the risk adjustment program, with the aim of reducing financial volatility.