Machine Learning and Automation in Healthcare Claims Processing

Abstract

Healthcare systems have evolved rapidly; driven by the need for efficient and accurate claims processing in order to reduce fraud, errors, and increase operational efficiency. “The existing traditional manual methods are error-prone, time-consuming and costlier in terms of administration. This chapter provides insight into how Machine Learning (ML) and Automation are fundamentally changing the way that healthcare claims are being managed in a revolutionary manner. We explore essential ML techniques in the form of predictive analytics, anomaly detection, natural language processing (NLP), and robotic process automation (RPA), that further enhance claims validation, fraud detection, and adjudication. This chapter explores various case studies and tangible implementations which have reported better claim accuracy, shorter processing times and increased fraud deterrent potential. Moreover, we explore challenges including data privacy, ethical implications, and regulatory compliance in AI-based automation. Further more, we discuss challenges such as data privacy, ethical considerations, and regulatory compliance in AI-driven automation. This will not only lead to a flowless operation and quicker settlement of the claim but also reduce the operational costs and, in return, improve the patient satisfaction in quality of care.