Risk adjustment for people with chronic conditions in private sector health plans.
BACKGROUND
Although the problem of adverse selection into more generous health insurance plans has been the focus of previous work, risk adjustment systems have only recently begun to be implemented to blunt its effect.


OBJECTIVE
This study examines the ability of the leading risk adjustment systems to predict health care expenditures for people with chronic conditions, using claims and enrollment data from 2 large employers.


METHODS
Predictive errors and total financial losses/gains are compared for different risk adjustment approaches (primarily hierarchical condition categories [HCCs] and adjusted clinical groups) for several chronic conditions.


RESULTS
One of the best performing risk adjustment systems was a regression-based HCC method, which had an average under-prediction error rate of 9% or 6%, depending on the employer. In comparison, more typical actuarial risk adjustments based on just age, gender, and prevailing area wages lead to a prediction error of at least 50%. We did not find evidence that payments for particular chronic conditions would be consistently and significantly under- or overestimated.


CONCLUSIONS
The leading risk adjustment approaches substantially reduce the incentives for adverse selection but do not eliminate them.
The MEDSTAT Group, Inc., Washington, DC, USA.
Tami L
TL
Mark
Ronald J
RJ
Ozminkowski
Adele
A
Kirk
Susan L
SL
Ettner
John
J
Drabek
D000293:Adolescent; D000328:Adult; D000368:Aged; D002908:Chronic Disease / Q000191:economics*; D003953:Diagnosis-Related Groups; D005260:Female; D005544:Forecasting; D006264:Health Benefit Plans, Employee / Q000191:economics* / Q000706:statistics & numerical data*; D005102:Health Expenditures; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D017059:Models, Econometric; D020379:Risk Adjustment; D014481:United States