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A Hierarchical Bayesian Model for Predicting the Rate of Nonacceptable In-Patient Hospital Utilization

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  • Rosenberg, Marjorie A
  • Andrews, Richard W
  • Lenk, Peter J

Abstract

A nonacceptable claim (NAC) is an insurance claim for an unnecessary hospital stay. This study establishes a statistical model that predicts the NAC rate. The model supplements current insurer programs that rely on detailed audits of patient medical records. Hospital discharge claim records are used as inputs in the statistical model to predict retrospectively the probability that a hospital admission is nonacceptable. A full Bayesian hierarchical logistic regression model is used with regression coefficients that are random across the primary diagnosis codes. The model provides better fits and predictions than standard methods that pool across primary diagnosis codes.

Suggested Citation

  • Rosenberg, Marjorie A & Andrews, Richard W & Lenk, Peter J, 1999. "A Hierarchical Bayesian Model for Predicting the Rate of Nonacceptable In-Patient Hospital Utilization," Journal of Business & Economic Statistics, American Statistical Association, vol. 17(1), pages 1-8, January.
    • Handle: RePEc:bes:jnlbes:v:17:y:1999:i:1:p:1-8
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    Cited by:

    1. Stijn Viaene & Guido Dedene, 2004. "Insurance Fraud: Issues and Challenges," The Geneva Papers on Risk and Insurance - Issues and Practice, Palgrave Macmillan;The Geneva Association, vol. 29(2), pages 313-333, April.
    2. Lynn Kuo & Jun Ying & Gim S. Seow, 2005. "Forecasting stock prices using a hierarchical Bayesian approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 24(1), pages 39-59.
    3. Yan Meng & Xueyan Zhao & Xibin Zhang & Jiti Gao, 2017. "A panel data analysis of hospital variations in length of stay for hip replacements: Private versus public," Monash Econometrics and Business Statistics Working Papers 20/17, Monash University, Department of Econometrics and Business Statistics.

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