Automating the Hospital Revenue Cycle: A Review of Artificial Intelligence and Robotic Process Automation Adoption in Claims Management, Denial Prevention, and Patient Billing in United States Hospitals

Olubusola Helen Odeyemi¹, Abiola Oluwaseun Idowu²

¹ Nashville General Hospital, 1818 Albion Street, Nashville, TN 37208, United States.

² Doctoral Program in Business Administration, Healthcare Administration, University of the Cumberlands, Williamsburg, Kentucky, United States.

Abstract

The administrative machinery that converts clinical care into hospital revenue in the United States consumes a share of national health expenditure that has no parallel among comparable health systems, and the revenue cycle sits at the center of that burden. This review synthesizes the peer-reviewed and authoritative grey literature on the adoption of artificial intelligence (AI), machine learning (ML), natural language processing (NLP), robotic process automation (RPA), and, most recently, generative AI across the hospital revenue cycle, with particular attention to claims management, denial prevention, and patient billing. A structured narrative review method was applied to the assembled literature, organized along two analytical axes: a four-level automation maturity framework (manual, rules-based, predictive, autonomous) and the sequential stages of the revenue cycle from patient access through payment resolution. The evidence indicates that automation has diffused unevenly: transactional front-end functions such as eligibility verification have approached rules-based saturation, while cognitively demanding mid-cycle functions such as clinical coding and denial appeals are only now crossing into predictive and generative maturity. Reported performance gains are consistent in direction but heterogeneous in magnitude, and few studies employ designs that support causal attribution. The review gives dedicated attention to safety-net and rural hospitals, for which thin operating margins convert modest denial-rate improvements into questions of institutional survival, yet which face the steepest barriers of capital scarcity, legacy system dependence, and workforce constraints. A synthesis of implementation barriers, a research agenda emphasizing calibration, equity, and total-cost-of-ownership evidence, and implications for hospital financial leadership are presented. The review concludes that revenue cycle automation is best understood not as a procurement decision but as an organizational capability whose value is contingent on data infrastructure, governance, and the redesign of financial work.

Keywords: Revenue cycle management, Hospital finance, Artificial intelligence, Robotic process automation, Claims denials, Patient billing, Administrative costs, Safety-net hospitals

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