Electronic Health Reporter

Healthcare organizations can no longer afford to treat revenue integrity as a problem to be addressed after a claim is denied. As reimbursement pressures intensify and both payers and providers turn to artificial intelligence to analyze claims, identify anomalies and automate processes, health systems need a more proactive approach to protecting revenue while maintaining compliance.

Ritesh Ramesh, CEO of MDaudit, believes that shift requires more than deploying new technology. It means bringing data, people and processes together across coding, compliance, auditing and revenue cycle operations, while using AI where it can deliver measurable business value.

Ramesh recently spoke with Electronic Health Reporter about the changing revenue integrity landscape, the growing “AI versus AI” dynamic between payers and providers, the importance of keeping human judgment in the loop, and what healthcare organizations should consider as they invest in AI-driven revenue cycle strategies.

Healthcare organizations have traditionally focused on managing denials after they occur. Why do you believe that approach is no longer sufficient in today’s reimbursement environment?

The reactive approach to denials management is no longer feasible, as the denial volumes, dollars per denial, and adjudication days per claim have grown incrementally over the years. According to our 2025 Benchmark Report, the average amount per coding-related denial increased 28% from 2023 to 2025. Health systems are under tremendous financial pressure due to a tighter reimbursement and policy environment, and a reactive approach will further stretch their already thin cash flow. Leveraging data, insights, and technology will help them proactively identify denial trends, fix issues before claims are paid, and stay two steps ahead of payers.

You’ve spoken about “Revenue Integrity Redefined.” What does that concept mean in practical terms for health systems, and why is now the right time to rethink traditional revenue integrity strategies?

Revenue integrity is the sustained alignment of three outcomes: reimbursement that reflects the care delivered, compliance that withstands payer and regulatory scrutiny, and operations efficient enough to hold both at enterprise scale. When you pursue any one of the three in isolation, the other two erode. With the advent of AI, now is the right time to transform the people and process dimensions so revenue integrity strategies can be successful. Technology can enable outcomes, but it cannot replace the people and process dimensions in the health system revenue cycle. Investments in technology should be balanced with process reengineering and upskilling people in new technologies.

Many healthcare organizations still treat revenue integrity as a department rather than an enterprise-wide strategy. What mindset shift needs to happen at the executive level?

Enterprises that drive successful revenue integrity strategies leverage a scalable technology platform; stand up a cross-functional program across billing compliance, coding, and revenue cycle; share insights and knowledge; track KPIs that matter; and make measurable progress toward the three outcomes: optimal reimbursement, compliance, and operational efficiency. They don’t see a successful revenue integrity program as a short-term, transactional approach to get claims paid today; they focus on fixing difficult, long-term processes and data issues across the revenue cycle continuum, so these issues don’t recur.

Some cross-functional programs need a strategic charter, actionable KPIs, change management, and business sponsorship to break departmental silos and office politics. This is where executives can drive their revenue integrity vision for the enterprise and align their respective teams to play together as one team.

AI and the Future of Revenue Cycle

Payers are increasingly using artificial intelligence to review claims and identify anomalies. How should providers respond to this changing landscape?

Providers must centralize their claim, payment, and clinical data and glean insights to identify issues and trends before payers report them. A continuous monitoring program and investments in technology platforms that can identify billing, coding, and payment anomalies in real time are the needs of the hour. This will allow them to respond to payers’ use of AI and their investments in this area in a timely and effective manner

AI has become one of the biggest topics in healthcare technology. Where do you see it delivering the greatest value in revenue cycle management today, and where do you think expectations may be getting ahead of reality?

AI has the highest potential to eliminate many manual administrative tasks and automate many parts of the healthcare revenue cycle. Many industry analysts see a world where the cost-to-collect metric will go down for health systems over the next decade because AI and agents help them keep more profits to invest in patient care. Lots of folks hear the word “autonomous” and think AI is a magic wand. I urge them to view AI like any other technology: it needs process and people to succeed. Many AI projects fail because of poor process design and a lack of talent upskilling.

Workforce shortages continue to challenge HIM, coding, and revenue cycle teams. How can AI and intelligent automation help organizations do more with limited resources while maintaining compliance and coding accuracy?

AI can really help with repetitive and mechanical tasks. It can scale exponentially across millions of rows of data and a voluminous number of tasks. When it goes properly, it’s beautiful to watch. When it fails, errors can be amplified, resulting in heavy financial losses. AI governance is critical. With a shortage of expertise in coding and revenue cycle, automation coupled with process and workflow redesign is critical to free those experts to focus on the parts of the process that require human judgment, including validating AI results and handling high-stakes areas.

Payers are increasingly using AI to identify potential claim issues, while providers are also turning to AI to improve audit readiness and revenue integrity. How is this accelerating “AI versus AI” dynamic changing the role of the human auditor, and why is keeping human judgment in the loop becoming more important rather than less?

AI can never be a substitute for human judgment and clinical expertise. It can be trained on large volumes of data sets to find patterns and help identify issues quickly so health systems can act. Providers must invest in AI to level the playing field and accelerate responses to payer queries and denials. Someone recently told me that AI will help them reduce denials. I replied that AI would help them defend the services provided, but it doesn’t expand the insurer’s risk premium pool, which can only pay a finite number of claims. It’s much more important for providers to defend the services they provided in days than for systems to integrate and analyze data for months. They will be left behind.

There has been a lot of discussion about AI adoption in healthcare, but less about proving measurable value. What does “Meaningful AI” mean in practice, and what should healthcare organizations look for to ensure AI investments deliver ROI, reduce operational friction, and strengthen decision-making rather than simply add another technology layer?

Meaningful AI is a framework with five components: data, models, workflow, security controls, and humans in the loop. The whole goal of this framework is to be pragmatic in leveraging AI in use cases with tangible business value. This can be reducing process friction, capturing revenue, or reducing risk. When designing an AI system, people should say, “This use case is not fit for AI because it does not produce any tangible benefits.” Meaningful AI brings that scrutiny and clarity to where AI is applied and what it produces for end users.

Leadership and Industry Outlook

Many healthcare organizations struggle with disconnected data across coding, CDI, compliance, auditing, and revenue cycle teams. How important is breaking down those silos to improving financial performance?

Organizations that achieve strong revenue integrity outcomes do so by combining a scalable technology platform with a coordinated, cross-functional program spanning billing compliance, coding, and revenue cycle operations. They foster knowledge sharing, monitor meaningful KPIs, and drive measurable progress toward three core objectives: optimal reimbursement, regulatory compliance, and operational efficiency.

Rather than treating revenue integrity as a short-term, transactional effort focused solely on getting claims paid, leading organizations take a broader view. They address the underlying process and data issues that create revenue leakage and compliance risks across the revenue cycle, ensuring those challenges are resolved permanently rather than repeatedly managed.

Effective cross-functional initiatives often require a clear strategic charter, actionable KPIs, strong change management, and executive sponsorship to overcome departmental silos and organizational politics. This is where leadership plays a critical role by establishing a unified revenue integrity vision and aligning teams across the enterprise to work collaboratively toward shared goals.

If you were advising a health system CFO making technology investments today, what capabilities would you consider essential for protecting both revenue and compliance?

I would advise CFOs to consider 1) deploying a data driven, proactive strategy to documentation, coding accuracy and denials management; 2) paying attention to payer behavior in real time to understand how best to adapt their RCM strategies; 3) keeping an open mind when reimagining your business processes and upskilling talent while deploying AI; and 4) investing in AI risk and governance as a mandatory function

Looking ahead five years, what do you think revenue integrity will look like, and what changes do you expect will have the biggest impact on healthcare organizations?

As healthcare organizations deploy AI over the next five years, there will be lots of learning from successes and losses.  Every health system hopes AI will automate administrative tasks, eliminate waste, and lower cost-to-collect so they can reinvest profits in patient access and care. Various timelines and expectations must be met over the next decade. I am optimistic that there will be many great success stories at the end for others to scale. The current health system infrastructure has been built over decades; it’s unfair to expect AI to perform its magic quickly. I will leave it at that.

Finally, what excites you most about the future of healthcare revenue cycle management and MDaudit’s role in helping organizations navigate that future?

At MDaudit, we are pragmatic about the use of AI in healthcare RCM. We’ve spent a lot of time thinking through which workflows and end users we can impact by leveraging AI. For every idea we accept, we reject 5-10 others.

Our customers work with us as co-creators to develop real products with AI functionality that work in their operational setting. They are the first to tell us what we tried just didn’t work.

We have been embedding Meaningful AI into the MDaudit platform for nearly three years to generate real value. In the last 12 months alone, we’ve generated more than $400 million in value for our customers associated with revenue retention, risk mitigation, and labor productivity.

We see so many future opportunities on our roadmap to continue delivering AI-enabled business value. Our whole organization is both excited about the potential of AI and aware of its risks. Luckily, our customers keep us grounded.