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Fraud detection in healthcare billing and claims
1 Department of Technology, Illinois State University, USA.
2 Department of Finance, Comprehensive Community Based Rehabilitation in Tanzania, Tanzania.
Review
International Journal of Science and Research Archive, 2024, 13(02), 3376-3395.
Article DOI: 10.30574/ijsra.2024.13.2.2606
Publication history:
Received on 18 November 2024; revised on 24 December 2024; accepted on 26 December 2024
Abstract:
Healthcare fraud in billing and claims is a pervasive issue, costing the United States healthcare system billions of dollars annually. This paper provides a comprehensive exploration of how advanced technologies such as data analytics, machine learning, and anomaly detection can effectively combat fraudulent practices, including upcoding, phantom billing, and duplicate claims. By leveraging healthcare claims data, predictive models are developed to detect suspicious patterns in real time, assign risk scores to providers, and flag high-risk claims for further investigation. This approach enhances fraud detection accuracy, minimizes false positives, and enables efficient resource allocation for fraud mitigation. The proposed solutions include AI-powered fraud detection tools, automated alert systems, and continuous model training to adapt to evolving fraud tactics. These tools, when integrated into the claims processing workflow, facilitate proactive fraud prevention by identifying anomalies and streamlining investigation processes. Operational measures such as provider risk scoring and robust data-sharing frameworks complement these technical innovations, creating a multi-layered defense strategy. Additionally, the paper emphasizes the importance of policy initiatives, including enhanced staff training and inter-organizational collaboration, to foster a culture of vigilance and compliance. By combining cutting-edge technology with sound operational practices, this research aims to significantly reduce healthcare fraud, enhance system efficiency, and rebuild trust within the healthcare industry. The insights and recommendations presented in this study have broad implications for policymakers, healthcare providers, and insurers seeking to implement cost-effective and scalable fraud prevention strategies.
Keywords:
Healthcare Fraud Detection; Billing and Claims Anomalies; Machine Learning in Healthcare; Predictive Analytics for Fraud Prevention; AI-Powered Fraud Detection Tools; Risk Scoring in Healthcare
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
