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

43 Pages Posted: 11 Sep 2017

See all articles by Keith M. Marzilli Ericson

Keith M. Marzilli Ericson

Boston University - Markets, Public Policy, and Law; National Bureau of Economic Research (NBER)

Kimberley Geissler

University of Massachusetts Amherst

Benjamin Lubin

Boston University - Questrom School of Business

Date Written: September 2017

Abstract

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. 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.

Suggested Citation

Ericson, Keith M. Marzilli and Geissler, Kimberley and Lubin, Benjamin, The Impact of Partial-Year Enrollment on the Accuracy of Risk Adjustment Systems: A Framework and Evidence (September 2017). NBER Working Paper No. w23765. Available at SSRN: https://ssrn.com/abstract=3035107

Keith M. Marzilli Ericson (Contact Author)

Boston University - Markets, Public Policy, and Law ( email )

Boston, MA
United States

National Bureau of Economic Research (NBER) ( email )

1050 Massachusetts Avenue
Cambridge, MA 02138
United States

Kimberley Geissler

University of Massachusetts Amherst ( email )

Department of Operations and Information Managemen
Amherst, MA 01003
United States

Benjamin Lubin

Boston University - Questrom School of Business ( email )

595 Commonwealth Avenue
Boston, MA MA 02215
United States

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