The sensitivity and specificity of forecasting high-cost users of medical care

Med Care. 1999 Aug;37(8):815-23. doi: 10.1097/00005650-199908000-00011.


Objectives: 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.

Publication types

  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adolescent
  • Adult
  • Aged
  • Aged, 80 and over
  • Case Management / statistics & numerical data
  • Case Management / trends
  • Child
  • Child, Preschool
  • Female
  • Forecasting*
  • Health Care Costs / statistics & numerical data
  • Health Care Costs / trends*
  • Health Services Needs and Demand / trends*
  • Humans
  • Infant
  • Infant, Newborn
  • Male
  • Middle Aged
  • Midwestern United States
  • Northwestern United States
  • ROC Curve
  • Risk Assessment / statistics & numerical data
  • Risk Assessment / trends
  • Sensitivity and Specificity
  • Technology, High-Cost / statistics & numerical data*