Upcoding: Evidence from Medicare on Squishy Risk Adjustment

Abstract

Risk adjustment is commonly used in health insurance markets to deal with problems of adverse selection and cream skimming by compensating health plans for insuring consumers whose diagnoses imply high expected costs. However, in all real world risk adjustment systems, it is the insurers themselves who report the diagnoses that determine risk scores. This creates incentives to “upcode” enrollees to extract higher payments. We model upcoding in the presence of adverse selection. Our model delivers a novel strategy for empirically separating upcoding from selection in aggregate, market-level data. We apply this strategy to analyze upcoding by Medicare Advantage plans. The results show that enrollees in Medicare Advantage plans generate 7% higher risk scores on average (and therefore 7% higher payments) than what the same enrollees would generate under Traditional Medicare. Absent a coding inflation correction, this implies a distortion in seniors’ choice between Medicare Advantage and Traditional Medicare, and excess payments to Medicare Advantage of $11.4 billion annually. We find that the degree of upcoding increases in the level of insurer-provider integration, suggesting that more integrated plans are better able to align incentives for their providers to code intensively. ∗We thank seminar participants at the 2014 American Society of Health Economists Bienniel Meeting, Southeastern Health Economics Study Group, RTI, Harvard Medical School, the University of Texas at Austin, Boston University, and the University of Illinois at Chicago for useful comments. We also thank Chris Afendulis, Francesco Decarolis, Randy Ellis, Tom McGuire, Joe Newhouse, and Daria Pelech for useful conversations. We thank Joshua Gottlieb and Colleen Carey for serving as discussants. We gratefully acknowledge financial support from the National Institute of Mental Health (Layton, T32-019733). †University of Texas at Austin and NBER. Email: mike.geruso@gmail.com ‡Harvard Medical School. Email: timothyjlayton@gmail.com