The sensitivity and specificity of forecasting high-cost users of medical care.
OBJECTIVE
This study compares the ability of 3 risk-assessment models to distinguish high and low expense-risk status within a managed care population. Models are the Global Risk-Assessment Model (GRAM) developed at the Kaiser Permanente Center for Health Research; a logistic version of GRAM; and a prior-expense model. GRAM was originally developed for use in adjusting Medicare payments to health plans.


METHODS
Our sample of 98,985 cases was drawn from random samples of memberships of 3 staff/group health plans. Risk factor data were from 1992 and expenses were measured for 1993. Models produced distributions of individual-level annual expense forecasts (or predicted probabilities of high expense-risk status for logistic) for comparison to actual values. Prespecified "high-cost" thresholds were set within each distribution to analyze the models' ability to distinguish high and low expense-risk status. Forecast stability was analyzed through bootstrapping.


RESULTS
GRAM discriminates better overall than its comparators (although the models are similar for policy-relevant thresholds). All models forecast the highest-cost cases relatively well. GRAM forecasts high expense-risk status better than its comparators within chronic and serious disease categories that are amenable to early intervention but also generates relatively more false positives within these categories.


CONCLUSIONS
This study demonstrates the potential of risk-assessment models to inform care management decisions by efficiently screening managed care populations for high expense-risk. Such models can act as preliminary screens for plans that can refine model forecasts with detailed surveys. Future research should involve multiple-year data sets to explore the temporal stability of forecasts.
Meenan
R T
RT
Center for Health Research, Kaiser Permanente, Northwest Division, Portland, OR 97227-1110, USA. richard.meenan@kp.org
O'Keeffe-Rosetti
C
C
Hornbrook
M C
MC
Bachman
D J
DJ
Goodman
M J
MJ
Fishman
P A
PA
Hurtado
A V
AV
D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D019090:Case Management / Q000706:statistics & numerical data / Q000639:trends; D002648:Child; D002675:Child, Preschool; D005260:Female; D005544:Forecasting; D017048:Health Care Costs / Q000706:statistics & numerical data / Q000639:trends*; D006301:Health Services Needs and Demand / Q000639:trends*; D006801:Humans; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D015146:Midwestern United States; D015205:Northwestern United States; D012372:ROC Curve; D018570:Risk Assessment / Q000706:statistics & numerical data / Q000639:trends; D012680:Sensitivity and Specificity; D013675:Technology, High-Cost / Q000706:statistics & numerical data*