Enhancing Prescription Fraud and Error Detection in NHS Prescriptions Through Anomaly Detection

Abstract

This research explores the critical issue of fraud and errors in NHS prescriptions through data-driven methods. It investigates the landscape of prescription fraud within the healthcare system, delves into existing fraud control mechanisms, and scrutinizes fraud detection methods encompassing manual inspection, rule-based techniques, and advanced machine learning algorithms. The research adopts a structured approach, comprising data understanding, preparation, and modeling phases. Performance metrics and machine learning algorithms, including Neural Networks, Decision Trees, and Regression models and traditional outlier detection methods are employed to develop a robust fraud detection model. The findings of this pioneering study hold promise for revolutionizing fraud prevention and detection within the NHS, ultimately leading to improved patient care and cost savings. With a steadfast commitment to realizing its objectives, this study also extends an invitation to explore potential avenues for future exploration, emphasizing the importance data literacy and expert insights for refining fraud detection strategies in the healthcare sector.