Report: AI and Automation to Reshape Revenue Cycle Management

What You Should Know: 

– A new report from Everest Group, supported by Omega Healthcare, highlights the transformative impact of artificial intelligence (AI) and automation on revenue cycle management (RCM) in healthcare. 

– The study, titled “Realizing the Promise of Tech-Enabled, AI-Driven Revenue Cycle Management (RCM): Outsourcing in the New Era,” reveals that 85% of senior healthcare executives believe AI will significantly improve RCM efficiency in the next five years.

Healthcare Leaders Embrace Strategic Outsourcing and Generative AI to Optimize RCM

The report surveyed 41 C-Suite executives (CTOs, CIOs, CEOs), senior vice presidents, and directors of RCM Operations at US-based healthcare provider organizations. The study included organizations of all sizes and types, such as health networks, independent hospitals, independent physicians and physician groups, and ambulatory surgical centers, and DMEs. 

Key findings of the report include: 

• Strategic Outsourcing Partnerships: Healthcare organizations are shifting towards more strategic RCM outsourcing partnerships to address challenges and optimize financial performance.
• Increased RCM Budgets: Over half of healthcare leaders expect increased RCM outsourcing budgets by 2030, driven by the potential of generative AI.
• Generative AI Adoption: 51% of healthcare providers are actively exploring or implementing generative AI in RCM, with use cases ranging from real-time eligibility verification to synthetic data generation.
• Investment Priorities: Cybersecurity is the top investment priority for 2025, while AI and automation are expected to dominate by 2030.

The Rise of Generative AI in RCM

Generative AI is poised to revolutionize RCM by automating manual processes, improving billing accuracy, and reducing claim denials. The top five generative AI use cases identified by RCM leaders include:

1. Real-time eligibility and benefits verification
2. Claims data analysis and pattern recognition
3. Generative AI chatbots for patient inquiries
4. AI-powered documentation improvement
5. Synthetic data generation and automated coding

Overcoming Barriers to Adoption

Despite the enthusiasm for generative AI, healthcare organizations face challenges in its adoption, including:

• Lack of in-house expertise: Around 80% of executives cite this as a key hurdle.
• Integration challenges: Integrating AI with existing EHR systems can be complex.
• Regulatory uncertainty: Some organizations are awaiting clearer regulatory guidelines before investing heavily in AI.

“The hope and promise of AI, gen AI and agentic AI are no longer just hype,” said Anurag Mehta, CEO and Co-Founder, Omega Healthcare.  “While the technology is important, it is only part of the equation. To fully harness the power of AI it will become increasingly critical for healthcare providers to work with the right partners.”