A recent survey by Inovalon, which included over 400 revenue cycle and financial executives and managers, highlights the potential of AI to improve revenue cycle management (RCM) in healthcare. According to the survey, 84% of respondents expressed optimism about the use of AI in hospitals’ RCM processes.
However, there are still concerns, with about a third of participants skeptical about AI’s accuracy, reliability, and lack of familiarity. Key concerns include AI being too new and untested, raising questions about its immediate practicality.
Human performance in RCM is still considered superior by 20% of respondents, suggesting a cautious approach to replacing human roles with AI. Julie Lambert, president and general manager at Inovalon, argues that AI and human expertise should work together rather than be seen as an “either/or” solution.
By combining both AI technology and human expertise, there is greater potential for improved outcomes in RCM, especially in areas that are labor-intensive and error-prone.
Lambert identified areas such as denials, prior authorizations, and eligibility as key pain points where AI could make the most impact. These areas are interconnected; for example, errors during the registration process often result in denials at a later stage. AI can help predict and prevent these denials by analyzing past scenarios and utilizing data to build and refine predictive models.
AI’s strength lies in its ability to continuously learn and adapt. According to Lambert, AI models must evolve by learning from real-time data and feedback. This dynamic approach ensures that the models are not static and can adjust to new data, helping RCM become more accurate and efficient over time.
AI also requires constant updates based on external factors, such as regulatory changes, to ensure that the models remain relevant and reliable.
Finally, Lambert emphasized that AI should be inclusive of all levels of the organization, not just the C-suite or data scientists. For AI to be successful, it must involve input from those who are directly handling the operations and data.
By involving everyone in the process, from data managers to workflow coordinators, AI can be better tailored to meet the practical needs of RCM, making the technology more effective and accessible across the healthcare sector.
