AI-driven Forecasting for Medicarerisk Score Modernization

Abstract

Medicare risk adjustment is a cornerstone of payment determination in Medicare Advantage, yet existing hierarchical condition category–based models face increasing limitations related to predictive accuracy, equity, and responsiveness to evolving healthcare delivery patterns. Traditional approaches rely primarily on retrospective claims data and linear modeling assumptions, constraining their ability to capture nonlinear relationships, temporal dynamics, and emerging risk signals. Advances in artificial intelligence (AI) and machine learning present opportunities to modernize Medicare risk score forecasting by improving precision and adaptability; however, their application within a federally regulated payment system introduces significant methodological, ethical, and policy challenges. This review synthesizes the literature on AI-driven forecasting methods relevant to Medicare risk adjustment, critically examining their performance advantages, risks of algorithmic bias, transparency and explainability constraints, coding incentives, and regulatory implications. Building on this synthesis, the article proposes a structured conceptual framework for AI-driven Medicare risk score modernization that integrates advanced predictive modeling with governance, fairness evaluation, auditability, and policy feedback mechanisms. The review concludes that hybrid, governance-aware AI approaches may enhance risk score accuracy while preserving equity, transparency, and accountability, offering a pragmatic pathway for responsible modernization of Medicare risk adjustment in an increasingly data-driven healthcare system.

Ähnliche Arbeiten