New Ambient Intelligence and Humanized Computing Study Findings Recently Were Reported by Researchers at National Institute of Technology (Provider Profiling and Labeling of Fraudulent Health Insurance Claims Using Weighted Multitree): Technology – Ambient Intelligence and Humanized Computing

2021 OCT 08 (NewsRx) -- By a News Reporter-Staff News Editor at Health Policy and Law Daily -- Fresh data on Technology - Ambient Intelligence and Humanized Computing are presented in a new report. According to news reporting originating from Tiruchirappalli, India, by NewsRx correspondents, research stated, “Recently, healthcare organizations getting engross in digitizing the health insurance system. Besides its undeniable benefits, the risk of exaggerating a claim or entirely fabricating one by providers is increasing.”

Financial support for this research came from Scheme for Promotion of Academic and Research Collaboration (SPARC) - Ministry of Human Resource Development, Government of India.

Our news editors obtained a quote from the research from the National Institute of Technology, “Provider profiling aids in outlier false claims by measure the performance of providers and outcomes of healthcare. Hence provider profiling has become an interesting research topic in the health insurance system. However, most of the existing provider profiling approaches are encountering the problem of intermediate results due to class overlappings. Another problem encounter in developing or validating an automated fraud detection model is the availability of labeled data. The manual labeling of huge claims data by medical experts is always not feasible. Hence, it is essential to automate the process of fraud detection which was not focused on by the researchers who are developing healthcare fraud detection models. There is one existing approach to automate the labeling of health insurance claims which considers the provider’s unique identification number as a reference while one-to-one mapping with real-world fraudulent claims. However, the approach is encountering a problem of missing values in providers’ identification numbers, causing poor performance in healthcare fraud detection models. In this study, we have proposed a Weighted MultiTree approach to mitigate the aforementioned problems of provider profiling and labeling. MultiTree is a DAG construction in which each node is reachable from any other node without ambiguity. And hence our proposed approach performed provider profiling without intermediate results with less construction cost.”

According to the news editors, the research concluded: “And the labeling of claims using unique details set of providers yielded from MultiTree enhanced the detection accuracy of fraudulent claims.”

This research has been peer-reviewed.

For more information on this research see: Provider Profiling and Labeling of Fraudulent Health Insurance Claims Using Weighted Multitree. Journal of Ambient Intelligence and Humanized Computing, 2021. Journal of Ambient Intelligence and Humanized Computing can be contacted at: Springer Heidelberg, Tiergartenstrasse 17, D-69121 Heidelberg, Germany. (Springer - www.springer.com; Journal of Ambient Intelligence and Humanized Computing - http://www.springerlink.com/content/1868-5137/)

The news editors report that additional information may be obtained by contacting G. R. Gangadharan, National Institute of Technology, Tiruchirappalli, India.

The direct object identifier (DOI) for that additional information is: https://doi.org/10.1007/s12652-021-03481-6. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.

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