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

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

Keith Marzilli Ericson

Kimberley H. Geissler

Benjamin Lubin

Accurate risk adjustment facilitates healthcare 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 individual is observed for a shorter time. Due to missed diagnoses, risk adjustment systems will underpay for partial-year enrollees, as compared to 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. Our result is innovative — we are unaware of a risk adjustment method that uses diagnosis information to adjust for partial-year enrollment in this way—and provides a potential solution for programs that have avoided using diagnosis data to risk adjust partial-year enrollees. A commonly cited reason that risk adjustment systems do not use data from actual partial-year enrollees is that sample sizes are too small to accurately calibrate the model for rare conditions. Because the scaling factor in our model is determined based on the probability the diagnosis is observed in the shorter versus longer observation period, simulations from data on full-year enrollees will provide the necessary information for all conditions to correct payments for partial year enrollees

After presenting our theoretical model, 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%. 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.