Abstract
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. 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. And the labeling of claims using unique details set of providers yielded from MultiTree enhanced the detection accuracy of fraudulent claims.
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Funding
This work is partially supported by the Scheme for Promotion of Academic and Research Collaboration (SPARC), sponsored by the Ministry of Human Resource Development, Government of India, under the project titled Digital Health Records Storage and Analysis for Healthcare Provisioning of Global Patients: An India-Australia Initiative (1406).
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Settipalli, L., Gangadharan, G.R. Provider profiling and labeling of fraudulent health insurance claims using Weighted MultiTree. J Ambient Intell Human Comput 14, 3487–3508 (2023). https://doi.org/10.1007/s12652-021-03481-6
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DOI: https://doi.org/10.1007/s12652-021-03481-6