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Artificial Intelligence in Healthcare Claims Processing: Automating Claim Validation and Fraud Detection

Authors: Veeravaraprasad Pindi

Country: India

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Abstract: This paper describes the development and use of artificial intelligence tools that were created to solve this industry issue. These models provide a rigorous check of the claims information and identify fraud through recognizing the affiliation and interaction of the parties constituting the claim (patient, providers and insurance company/insured), as well as the service and diagnosis codes. This paper shows that it is possible to predict claim denials using information freely available to providers, a fact that would allow them to make better decisions with regard to their claims and the financial health of their organizations. These models can provide a new view on enhancing the claims management and will contribute to the decrease of the number of denied claims. It was stated that currently Medicare fee-for-service claims have been denied annually approximately 10 percent which places a heavy burden on both administration and financial aspects of care delivery settings. It is crucial that a proper denial management plan be put in place to assess the major reasons of denied claims so as to have better chances of initial payment of a claim [1]. However, because of the high incidence of claims and a scarcity of funds to deal with denials, the issue of priorities in the allocation of funds becomes pertinent. In this work, several categories of machine learning models are built to determine the likelihood of a given claim to be denied prior to being filed, and the datasets comprise both the provider and beneficiary levels. These solutions assist in ensuring that health claims are processed effectively and efficiently hence reducing costs to the payers as well as the providers. It has been observed that the incorporation of these AI models has helped in claim processing reductions hence increasing on efficiency levels of patients. Moreover, with the help of the machine learning techniques used, the models get more efficient in recognizing fake claims that leads to a more efficient and sustainable healthcare system. Currently, approximately $180 billion is channelled towards insurance claims processing in the United States’ health insurance industry alone [1,2]. These costs are primarily a direct result of the significant activity in administratively verifying the claims’ correctness. Nevertheless, the largest costs are associated with the fact that up to 35% of payments are considered incorrect. It incorporates omission of correcting errors in underpayments and overpayments, rejection of benefits among others. Failures in payments most of the time arise from eligibility, referrals, authorizations, and utilization. Their higher susceptibility to fraud means a higher percentage of the estimated between $11 and $54 billion in the industry annually belongs to incorrect payments. This has largely been due to the escalating rates in healthcare costs and the ever-growing public expectations to offer better services while combating the act of fraud. The decision and management of such corrective action rest squarely on the payers’ shoulders [2]. Unleashing Evidence-Based Medicine Review: Close to 90% of the administrators and other employees of the healthcare entities report that they spend a lot of their time dealing with the different administrative and financial issues. The reimbursement of healthcare claims is one of the most expensive and time-consuming activities that both the payers and the providers experience on a routine basis. Employers usually invest huge sums of money to process claims and even then, the majority of the claims payments are inaccurate.

Keywords: Healthcare Claims Processing, Medicare, Batch Processing, Error Reduction, Technology, Artificial Intelligence (AI), Machine Learning (ML), Blockchain, Claims Automation, Data Validation

Paper Id: 1775

Published On: 2015-10-13

Published In: Volume 3, Issue 5, September-October 2015

Cite This: Artificial Intelligence in Healthcare Claims Processing: Automating Claim Validation and Fraud Detection - Veeravaraprasad Pindi - IJIRMPS Volume 3, Issue 5, September-October 2015.

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