Abstract

Healthcare organizations often find it hard to control costs amid increasing expenses and lower reimbursement rates while balancing patient care. To be financially stable, healthcare organizations must keep costs in check without hurting patient outcomes. Using Artificial Intelligence (AI) in healthcare may change how money and patient care are managed. AI can help make healthcare finance models work better by improving efficiency, managing resources, and creating effective cost-saving plans while ensuring care quality remains high. Predictive analytics in healthcare finance uses data, statistical methods, and machine learning to predict financial results, better allocation of resources, and improve decision-making in healthcare organizations. By looking at past data, predictive models can estimate revenue, find ways to cut costs, predict payment delays, and evaluate risks, which improves the financial health of healthcare institutions.

This paper looks at how AI can improve healthcare financial models, especially in balancing expenses and quality care, as well as what challenges and chances come with this integration. This paper also reviews how predictive analytics is applied in healthcare finance, noting its main benefits, hurdles, and future outlook. We show examples where predictive analytics has been used successfully and discuss how it can affect managing healthcare costs, enhancing revenue cycles, and promoting value-based care.