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A review and comparison of medical expenditures models: two neural networks versus two-part models

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This paper compares the two-part model (TPM) that distinguishes between users and non-users of health care, with two neural networks (TNN) that distinguish users by frequency. In the model comparisons using data from the National Health Research Institute (NHRI) in Taiwan, we find strong evidence in favor of the neural networks approach. This paper shows that the individuals in the self-organizing map (SOM) network clusters can be described as several different forms of frequency distributions. The integration model of SOM and back propagation network (BPN) proposed by this paper not only permits policymakers to easily include more risk adjusters besides the demographics in the traditional capitation formula through the adaptation and calculation power of neural networks, but also reduces the incentives for cream skimming by decreasing estimation biases.

Keywords: health risk; medical expenditures; risk adjustor; two neural networks; two-part model

Document Type: Research Article


Affiliations: 1: Department of Risk Management and Insurance, National Kaohsiung First University of Science and Technology, Kaohsiung, Taiwan 2: Graduate School of Business Administration, Kobe University, Kobe, Japan

Publication date: January 1, 2008

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