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Neuro-fuzzy CBR hybridization: Healthcare application

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1 Author(s)
Woodside, J.M. ; Dept. of Comput. & Inf. Sci., Cleveland State Univ., Cleveland, OH

As the total cost of healthcare continues to rise, computerized methods are sought to improve the overall efficiency and effectiveness of healthcare systems. In this application, the focus is on healthcare claim payment processing, which is a major component of administrative healthcare costs. Due to the complexity of healthcare data, current methods require a large amount of healthcare claim payment processing to occur through manual intervention by human operators. This limitation necessitates the inclusion of machine learning techniques to create a hybrid system for automation of healthcare claim payments. Further automation of claims payment processing will lead to improved quality cost components of healthcare delivery. Machine learning techniques are used to demonstrate the feasibility of a hybrid system for healthcare claim payment automation, leading to reduced administrative costs and increased efficiencies. When the administrative cost savings are applied to the industry, this contributes to lowering the overall cost of healthcare.

Published in:

Neural Networks, 2008. IJCNN 2008. (IEEE World Congress on Computational Intelligence). IEEE International Joint Conference on

Date of Conference:

1-8 June 2008