Leveraging Machine Learning and Artificial Intelligence to Revolutionize Transparency and Accountability in Healthcare Billing Practices across the United States

Abstract

The rising complexity and opacity of healthcare billing practices in the United States have led to significant financial burdens on patients and insurers. Leveraging Machine Learning (ML) and Artificial Intelligence (AI) offers transformative solutions to enhance transparency and accountability in medical billing. This study explores how AI-driven automation, predictive analytics, and natural language processing (NLP) can detect fraud, optimize claims processing, and ensure compliance with regulatory standards (Ahmed et al., 2022). By analyzing historical billing patterns and identifying discrepancies, AI can mitigate billing fraud, reduce administrative costs, and improve financial efficiency in healthcare systems (Bhardwaj et al., 2023). Additionally, ML algorithms can enhance price transparency by providing real-time cost estimations, thus empowering patients to make informed decisions (Singh & Wang, 2023). Ethical considerations, including bias in AI models and data privacy, are also examined (Chowdhury & Roberts, 2021). The findings underscore the potential of AI and ML in revolutionizing healthcare billing practices, making them more equitable, efficient, and accountable. Future research should focus on refining AI models for fairness, integrating block chain for added security, and developing regulatory frameworks to support AI-driven billing solutions (Lee et al., 2022).