Can Big Data Cure Risk Selection in Healthcare Capitation Programs? A Game Theoretical Analysis

Abstract

Early empirical evidence indicates that Medicare Advantage (MA), the largest capitation payment program in the U.S. healthcare market, unintentionally incentivizes health plans to cherry pick profitable patient types, which is referred to as "risk selection". Motivated by this observation, we study the root causes of risk selection in the MA market design and potential strategies to eliminate risk selection. The existing literature primarily attributes the observed risk selection in MA market to data limitations and low explanatory power (e.g. low R^2) of the current risk adjustment design in the MA market. With the availability of big data and advancements in machine learning (ML) techniques, risk selection due to imperfect risk adjustment is expected to gradually disappear from the MA market. However, our study shows that big data and ML alone cannot cure risk selection in the MA capitation program. More specifically, we show that even if the current MA risk adjustment design becomes informationally perfect (e.g. R^2=1) through availability of big data and advanced ML algorithms, health plans still have incentives to conduct risk selection through strategically subsidizing some subgroups of patients using capitation payments collected from other subgroups, which we call "risk selection induced by cross subsidization". Furthermore, we develop and present selection-proof capitation mechanisms to eliminate this type of risk selection behavior from the MA market. Our findings further indicate that through some small modifications to the existing Medical Loss Ratio (MLR) mechanism, risk selection of this kind could be eliminated from the MA market.