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Predicting cost for new HMO subscribers.

B J Volicer1, D M Romagnoli

  • 1Department of Health, College of Health Professions, University of Lowell, MA 01854.

Health Services Research
|December 1, 1988
PubMed
Summary
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A new model predicts health maintenance organization (HMO) subscriber costs using sex and coverage type, explaining 20% of cost variation. The model accurately forecasts costs for companies, especially those with lower average expenses.

Area of Science:

  • Health Economics
  • Predictive Modeling
  • Healthcare Management

Background:

  • Healthcare costs are a significant concern for Health Maintenance Organizations (HMOs).
  • Accurate cost prediction is crucial for financial planning and subscriber acquisition.
  • Previous models may not have fully captured cost drivers for new enrollees.

Purpose of the Study:

  • To develop and validate a predictive cost model for potential new HMO subscribers.
  • To identify key demographic and coverage-related factors influencing subscriber costs.
  • To assess the model's predictive accuracy across different employer groups.

Main Methods:

  • Regression analysis was performed on aggregated clinic, referral, and hospital cost data from 1985.
  • Log transformation of cost data was used to account for cost distribution.

Related Experiment Videos

  • Model performance was evaluated by correlating predicted costs with observed costs for 28 large companies.

    • Main Results:

    • Sex and coverage type explained 20% of the variation in subscriber costs.
    • Subscriber age was a significant predictor but not when controlling for coverage type.
    • The developed model showed a strong correlation (r = .75, p < .01) between predicted and observed costs.
    • Prediction accuracy was higher for companies with lower observed mean costs.

    Conclusions:

    • A robust model for predicting new HMO subscriber costs has been developed.
    • Sex and coverage type are key determinants of healthcare costs for HMO enrollees.
    • The model demonstrates practical utility in forecasting costs for large employer groups.