Financial data analytics in healthcare: A review of approaches to improve efficiency and reduce costs

Abstract

This review explores the role of financial data analytics in healthcare, focusing on its potential to improve operational efficiency and reduce costs. It examines current approaches such as predictive analytics, machine learning, and artificial intelligence, highlighting how these tools are used in areas like cost management, resource allocation, and revenue cycle optimization. While financial data analytics offers numerous benefits, including better decision-making and enhanced resource utilization, several challenges persist, such as data privacy concerns, system integration issues, high technology costs, and a shortage of skilled personnel. The paper also identifies opportunities for future advancements, including the adoption of cloud-based analytics, improved interoperability, and enhanced workforce training. The review concludes with recommendations for addressing these challenges and maximizing the impact of financial data analytics to promote more efficient, cost-effective healthcare delivery.