The Impact of Partial-Year Enrollment on the Accuracy of Risk-Adjustment Systems: A Framework and Evidence

Accurate risk adjustment facilitates health-care market competition. Risk adjustment typically aims to predict annual costs of individuals enrolled in an insurance plan for a full year. However, partial-year enrollment is common and poses a challenge to risk adjustment, since diagnoses are observed with lower probability when an individual is observed for a shorter time. Because of missed diagnoses, risk-adjustment systems will underpay for partial-year enrollees, as compared with full-year enrollees with similar underlying health status and usage patterns. We derive a new adjustment for partial-year enrollment in which payments are scaled up for partial-year enrollees’ observed diagnoses, which improves upon existing methods. We simulate the role of missed diagnoses using a sample of commercially insured individuals and the 2014 Marketplace risk-adjustment algorithm and find the expected spending of six-month enrollees is underpredicted by 19 percent. We then examine whether there are systematically different care usage patterns for partial-year enrollees in this data, which can offset or amplify underprediction due to missed diagnoses. Accounting for differential spending patterns of partial-year enrollees does not substantially change the underprediction for six-month enrollees. However, one-month enrollees use systematically less than one-twelfth the care of full-year enrollees, partially offsetting the missed-diagnosis effect.

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