PATIENT CARE AND FINANCIAL INTEGRITY IN HEALTHCARE BILLING THROUGH ADVANCED FRAUD DETECTION SYSTEMS

Abstract

Healthcare fraud poses a significant challenge, leading to substantial financial losses and compromising the quality of patient care. This study assesses the efficacy of advanced fraud detection systems, including data analytics, machine learning, predictive modeling, and natural language processing (NLP), in enhancing the detection and prevention of fraudulent activities in healthcare. By leveraging these technologies, healthcare organizations can process large volumes of complex data, adapt to evolving fraud patterns, and provide real-time monitoring. The findings indicate that data analytics effectively uncovers hidden patterns and anomalies, while machine learning and AI improve predictive accuracy by continuously learning from historical data. Predictive modeling enables proactive fraud prevention by forecasting potential fraud scenarios, and NLP extends detection capabilities to unstructured data such as clinical notes. The integration of these advanced technologies has resulted in significant financial savings and improved patient care, as demonstrated by case studies highlighting substantial reductions in fraudulent claims. The study concludes that adopting advanced fraud detection systems is essential for maintaining financial integrity and ensuring high-quality patient care in the evolving healthcare landscape.