Impact of AI-Enabled Revenue Cycle Management on Financial Performance and Patient Outcomes in U.S. Oncology Practices

Abstract

Artificial Intelligence is transforming the face of healthcare, especially in areas like Revenue Cycle Management (RCM), helping oncology practices, which often face high treatment costs and decreasing reimbursement rates. This study examines the financial and patient-related impacts of implementing Artificial Intelligence-supported RCM systems through the lenses of Resource-Based Theory and Systems Theory. These systems use predictive analytics, machine learning, and automation to manage billing, reduce costly errors, and improve workflow efficiency. As a result, they increase operational effectiveness and allow practices to dedicate more time to patient care. Major findings revealed significant improvements, indicating a reduction in claim denial rates, an accelerated cycle time, and improved patient satisfaction. Several oncology practice settings, including urban, rural, and remote, demonstrate the possibility of AI-enhanced RCM implementation. However, challenges such as high implementation costs, data privacy concerns, and staff resistance to change must be also be considered. Implementation strategies are based on phased models, staff training and development of policies and procedures to advocate for regulatory and financial support and performance monitoring. With the aim of solving these challenges, it is possible to realize the potential of AI-enhanced RCM for improved oncology practice and healthcare stability within the US and beyond.