Texas USA.
Research Article
World Journal of Advanced Research and Reviews, 2022, 13(03), 563–576
Received on 27 January 2022; revised on 17 March 2022; accepted on 20 March 2022
Healthcare insurance systems, especially in regions like India, face persistent challenges in cost management, resource allocation, and the timely processing of claims and data. This paper introduces an innovative data-driven approach to healthcare insurance that harnesses Big Data Analytics combined with a novel analytical framework designed for greater operational efficiency. We propose a hybrid method integrating Set Theory, MapReduce, Association Rule Mining, and MongoDB to create a robust model for healthcare insurance data analytics. Our method, developed to analyze large datasets accurately, aims to reduce processing delays and enhance decision-making by identifying patterns in patient claims and provider submissions. A prototype was developed using Java and evaluated on data from the National Health Insurance Scheme (NHIS) in India, achieving an accuracy rate of 98.3%, a notable improvement over existing models. The results demonstrate significant reductions in delays within data processing, streamlining both resource flow and data management. This model not only enhances data accuracy but also contributes to a more sustainable healthcare financing structure, marking a step forward in aligning healthcare services with modern technological advances.
Big Data; Healthcare; Insurance; Cloud; Data Base.
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Lakshmi Narasimhan Srinivasagopalan. Leveraging big data analytics for enhanced healthcare insurance: A new computational approach to efficiency and cost reduction. World Journal of Advanced Research and Reviews, 2022, 13(3), 563-576. Article DOI: https://doi.org/10.30574/wjarr.2022.13.3.0115