Statistical Modelling 17 (3) (2017), 196–222
Medical overpayment estimation: A Bayesian approach
Rasim M. Musal
Department of Computer Information Systems and Quantitative Methods,
McCoy College of Business,
Texas State University,
San Marcos, Texas,
USA
e-mail: rm84@txstate.edu
Tahir Ekin
Department of Computer Information Systems and Quantitative Methods,
McCoy College of Business,
Texas State University,
San Marcos, Texas,
USA
Abstract:
Overpayment estimation using a sample of audited medical claims is an often used method to determine recoupment amounts. The current practice based on central limit theorem may not be efficient for certain kinds of claims data, including skewed payment populations with partial overpayments. As an alternative, we propose a novel Bayesian inflated mixture model. We provide an analysis of the validity and efficiency of the model estimates for a number of payment populations and overpayment scenarios. In addition, learning about the parameters of the overpayment distribution with increasing sample size may provide insights for the medical investigators. We present a discussion of model selection and potential modelling extensions.
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