Using risk-adjustment models to identify high-cost risks.
BACKGROUND
We examine the ability of various publicly available risk models to identify high-cost individuals and enrollee groups using multi-HMO administrative data.


METHODS
Five risk-adjustment models (the Global Risk-Adjustment Model [GRAM], Diagnostic Cost Groups [DCGs], Adjusted Clinical Groups [ACGs], RxRisk, and Prior-expense) were estimated on a multi-HMO administrative data set of 1.5 million individual-level observations for 1995-1996. Models produced distributions of individual-level annual expense forecasts for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution. The area under the receiver operating characteristic curve (AUC) for "high-cost" prevalences of 1% and 0.5% was calculated, as was the proportion of "high-cost" dollars correctly identified. Results are based on a separate 106,000-observation validation dataset.


RESULTS
For "high-cost" prevalence targets of 1% and 0.5%, ACGs, DCGs, GRAM, and Prior-expense are very comparable in overall discrimination (AUCs, 0.83-0.86). Given a 0.5% prevalence target and a 0.5% prediction threshold, DCGs, GRAM, and Prior-expense captured $963,000 (approximately 3%) more "high-cost" sample dollars than other models. DCGs captured the most "high-cost" dollars among enrollees with asthma, diabetes, and depression; predictive performance among demographic groups (Medicaid members, members over 64, and children under 13) varied across models.


CONCLUSIONS
Risk models can efficiently identify enrollees who are likely to generate future high costs and who could benefit from case management. The dollar value of improved prediction performance of the most accurate risk models should be meaningful to decision-makers and encourage their broader use for identifying high costs.
Meenan
Richard T
RT
Center for Health Research, Northwest and Hawaii, Kaiser Permanente Northwest, Portland, Oregon 97227, USA. richard.meenan@kpchr.org
Goodman
Michael J
MJ
Fishman
Paul A
PA
Hornbrook
Mark C
MC
O'Keeffe-Rosetti
Maureen C
MC
Bachman
Donald J
DJ
D000293:Adolescent; D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D019090:Case Management / Q000191:economics; D002648:Child; D002675:Child, Preschool; D005260:Female; D017048:Health Care Costs; D006279:Health Maintenance Organizations / Q000191:economics*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D020379:Risk Adjustment; D012307:Risk Factors