Date of Award
Doctor of Philosophy (PhD)
College of Computing and Engineering
AI/RPA integration, artificial intelligence (AI), healthcare revenue cycle, revenue cycle management, robotic process automation (RPA)
There is a lack of understanding of specific risks and benefits associated with AI/RPA implementations in healthcare revenue cycle settings. Healthcare companies are confronted with stricter regulations and billing requirements, underpayments, and more significant delays in receiving payments. Despite the continued interest of practitioners, revenue cycle management has not received much attention in research. Revenue cycle management is defined as the process of identifying, collecting, and managing the practice’s revenue from payers based on the services provided.This dissertation provided contributions to both areas, as mentioned above. To accomplish this, a semi-structured interview was distributed to healthcare executives. The semi-structured interview data obtained from each participant underwent a triangulation process to determine the validity of responses aligned with the extant literature. Data triangulation ensured further that significant themes found in the interview data answered the central research questions. The study focused on how the broader issues related to AI/RPA integration into revenue cycle management will affect individual organizations. These findings also presented multiple views of the technology’s potential benefits, limitations, and risk management strategies to address its associative threats. The triangulation of the responses and current literature helped develop a theoretical framework that may be applied to a healthcare organization in an effort to migrate from their current revenue management technique to one that includes the use of AI/ML/RPA as a means of future cost control and revenue boost.
Leonard J. Pounds. 2021. A Framework for Artificial Intelligence Applications in the Healthcare Revenue Management Cycle. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, College of Computing and Engineering. (1165)