Health Insurance Fraud – Deception and Its Ramifications

SUMMARY

“Health Insurance Fraud – Deception and Its Ramifications” explores the pervasive issue of health insurance fraud, detailing its various forms such as double billing, phantom billing, unbundling, upcoding, and false diagnoses, and examining their significant impact on healthcare costs, premiums, and patient care. The book also covers the legal framework surrounding fraud and how to report it through insurance providers, state fraud bureaus, and national organizations. It then delves into the role of machine learning (ML) in detecting and preventing fraud, explaining different algorithms such as decision trees, neural networks, and reinforcement learning. Through case studies, the book demonstrates real-world applications of these technologies. Additionally, it outlines protection strategies like cybersecurity and best practices for safeguarding sensitive health data. The final chapter addresses the ethical, legal, and societal implications of using AI in fraud detection, discussing privacy concerns, regulatory compliance, and the importance of collaboration between stakeholders. The book offers a comprehensive approach to combating health insurance fraud using modern technologies while considering ethical and legal dimensions.