The Prior Authorization Revolution: How Machine Learning is Streamlining Healthcare's Most Costly Bottleneck

Before treating a patient, Prior Authorization requires healthcare providers to contact payers to obtain approval for a specific service or medication. Although it was meant to limit wasteful healthcare costs, PA has become a significant barrier, slowing patient care, adding costs, and making physicians' jobs more stressful. This study examines the growing use of machine learning (ML) as an effective solution to current challenges. ML boosts the PA process by automatically collecting data, providing estimates for approvals, and simplifying the entire process. The work examines the current state of PA, explores various applications of ML in this field, shares real-life examples, highlights the clear benefits, considers the broader implications of ML, and discusses the ethical issues and challenges that arise from using ML for PA. This research suggests that when ML is used in prior authorization, it is a breakthrough that can improve both the care provided and the sustainability of healthcare.