**Exploring Strategic Insights: Interview with Thomas Thatapudi, CIO of AGS Health**

Healthcare Revenue Cycle Management: AI Adoption Accelerates Despite Historic Tech Lag

New York, NY – August 19, 2025 – The healthcare industry, frequently enough criticized for its slow adoption of technological advancements, is now rapidly exploring the potential of Artificial Intelligence (AI) to revolutionize revenue cycle management (RCM). A recent conversation with Thomas Thatapudi, MBA, CIO of AGS Health, a firm serving major healthcare providers like Mayo Clinic and Cleveland Clinic, reveals a sector facing mounting pressures and turning to AI for solutions. The company, employing approximately 15,000 people, is a tech-first services provider focused on improving financial processes within healthcare systems.

The Pain Points Driving AI Investment

Thatapudi, who has spent over two decades in technology, emphasizes that RCM has historically lagged behind other areas of healthcare in technology investment. He notes that the industry’s labor-intensive nature and limited resources necessitate new toolsets, with AI emerging as a crucial component. A important driver is the increasing complexity and frequency of claim denials, making it unsustainable for providers and RCM companies to rely solely on manual processes.

“Ther is no way that providers can throw unlimited resources at it, and neither can the RCM providers like AGS Health,” Thatapudi stated. “Therefore, tech infusion is critical throughout the entire RCM lifecycle to prevent errors and ensure accurate billing and collections.” according to a recent report by the American Hospital Association, hospital operating margins remain under pressure, highlighting the need for efficiency gains.

Balancing Cost Reduction and Revenue enhancement

Healthcare organizations are approaching AI with a dual focus: reducing operational costs and maximizing revenue. Chief Financial Officers are demanding measurable returns, pushing for solutions that can lower the cost of collecting payments – currently averaging around 7 cents per dollar collected – while simultaneously improving efficiency. This pressure is compelling providers to seriously evaluate AI’s capabilities.

However, Thatapudi observes a spectrum of engagement, with some organizations simply attempting to capitalize on the “AI boom” without a clear understanding of implementation. others are proactively seeking to leverage AI for a competitive advantage.As of Q2 2025, industry analysts estimate that AI-powered RCM solutions represent a $2.5 billion market, projected to grow to $7 billion by 2030.

Consumerism and the Patient Experience in RCM

The intersection of RCM and patient consumerism presents a unique challenge. Frequently enough, a key step in the process involves informing patients about denied procedures or required changes to their care plans. Traditional methods, such as phone calls, frequently fail due to patients not recognizing unfamiliar numbers or simply not being available. This scenario highlights the need for more effective communication channels.

“These are patients who most problably have been waiting for that particular procedure for a long time,” Thatapudi explained.”How do you actually reach out to the patient and make sure their interaction with the healthcare system is seamless?” He pointed to the widespread adoption of mobile technology and social media as benchmarks for patient engagement, suggesting that healthcare should offer a similarly convenient and responsive experience.

The Rise of Agentic AI in Healthcare

Agentic AI, self-operating AI systems capable of independent problem-solving, is gaining traction within the healthcare sector, particularly among payers. These systems are being explored for tasks such as handling initial provider inquiries and automating responses to common questions. Payers are increasingly looking to agentic AI to alleviate the burden on their contact centers, which often face staffing constraints during periods of high claim volume.

Thatapudi anticipates that within the next 12 to 24 months, payers will begin deploying agentic AI to address provider questions, with potential expansion to member interactions.He shared a personal anecdote about a positive experience with an AI agent handling a home insurance claim, demonstrating the potential for empathetic and effective AI-driven customer service.

Area of RCM	Traditional Approach	AI-Powered Approach
Claim Denial Management	Manual review and appeals	AI-driven denial prediction and automated appeal generation
Prior Authorization	Manual submission and follow-up	AI-assisted automated submission and real-time status tracking
Coding	Manual coding by certified professionals	AI-assisted coding with human review

Did you No?: A recent study by McKinsey found that AI has the potential to create $350 billion to $410 billion in value in the U.S. healthcare system annually.

The Human-AI Partnership

Thatapudi believes the future of RCM lies in a collaborative approach, with AI augmenting rather than replacing human expertise. He envisions a workflow where AI handles routine tasks, while humans focus on complex cases requiring judgment and intuition. AGS Health is currently piloting a denial workflow that combines AI-generated denial letters with human review and editing, creating a feedback loop that continuously improves accuracy and effectiveness.

“Work will get delivered as a combination of humans and AI,” he asserted. “Sometimes AI work being audited by humans and vice versa. They are correcting each other, learning from each other.”

Strategic AI Investments and Risk Management

Companies are carefully evaluating AI investments, recognizing the potential for failure. AGS Health, recently acquired by Blackstone, is focusing on a limited number of strategic initiatives – denial management, contact center disruption, and autonomous coding – and accepting a potential 30-50% failure rate as part of the innovation process.

Pro Tip: Don’t try to boil the ocean. Focus on specific areas where AI can deliver the most impactful results.

Ultimately, Thatapudi emphasizes that the success of any technology, including AI, hinges on its ability to solve real customer problems and deliver economic value. The key question remains: can it collect dollars faster and more economically?

Looking Ahead: The Long-Term Impact of AI on RCM

The integration of AI into healthcare revenue cycle management is not simply a technological upgrade; it represents a essential shift in how healthcare organizations operate. As AI models become more elegant and data availability increases, we can expect to see even more innovative applications emerge, ultimately leading to a more efficient, clear, and patient-centric healthcare system. The challenge will be to navigate the ethical and regulatory considerations associated with AI, ensuring that these technologies are deployed responsibly and equitably.

Frequently Asked Questions about AI in Revenue Cycle Management

• What is revenue cycle management? Revenue cycle management encompasses all the administrative and clinical functions that contribute to capturing payment for patient services.
• how can AI help reduce claim denials? AI algorithms can identify patterns in denied claims and predict potential issues, allowing for proactive correction and submission.
• Is AI likely to replace human workers in RCM? The consensus is that AI will augment human capabilities, automating routine tasks and freeing up staff to focus on more complex issues.
• What are the biggest challenges to AI adoption in healthcare? Data privacy concerns,regulatory hurdles,and the need for skilled personnel are key challenges.
• How are payers using AI in revenue cycle management? Payers are leveraging AI to automate claim processing, detect fraud, and improve customer service.
• What is agentic AI and how does it apply to healthcare? Agentic AI refers to AI systems that can operate autonomously to solve problems, streamlining operations like answering provider inquiries.
• What should healthcare organizations consider when investing in AI for RCM? Focus on clear problem statements,prioritize strategic initiatives,and accept the possibility of some failures.

What are your thoughts on the role of AI in healthcare? Share your comments below!

What specific strategies does AGS Health employ to ensure data standardization beyond adhering to technical standards like ICD-10 and SNOMED CT?

Teh Evolving Role of the Healthcare CIO

Thomas Thatapudi, CIO of AGS Health, a leading provider of revenue cycle and clinical documentation advancement solutions, offers a unique perspective on the challenges and opportunities facing healthcare technology leaders today. His insights, gleaned from years of experience navigating the complexities of the healthcare landscape, are notably relevant as the industry undergoes rapid digital transformation. This article delves into key takeaways from our conversation, focusing on healthcare IT strategy, digital health innovation, and the critical role of data analytics in healthcare.

Navigating the Interoperability Challenge

One of the most persistent hurdles in healthcare IT remains interoperability. Thatapudi emphasizes that true interoperability isn’t simply about exchanging data; it’s about understanding that data.

HL7 FHIR (Fast Healthcare Interoperability Resources): He highlights FHIR as a crucial standard, enabling more seamless data exchange. “FHIR is a game-changer,but adoption requires a concerted effort across the industry. It’s not enough for a single organization to implement it; we need widespread participation.”

API Integration: AGS Health leverages APIs extensively to connect with various EHR (Electronic Health Record) systems and other healthcare platforms. This allows for a more fluid exchange of information, improving efficiency and accuracy.

Data Standardization: Beyond technical standards, Thatapudi stresses the importance of data standardization. “Without consistent data definitions, even perfectly interoperable systems can produce conflicting or misleading results.” This includes adhering to standards like ICD-10 and SNOMED CT.

The Rise of Cloud Computing in Healthcare

Cloud adoption in healthcare has accelerated substantially in recent years, driven by the need for scalability, cost-effectiveness, and enhanced security. Thatapudi discusses AGS Health’s journey to the cloud.

Security Concerns: Addressing security concerns is paramount. AGS Health prioritizes HIPAA compliance and employs robust security measures, including encryption, access controls, and regular security audits. “We view security not as a constraint, but as a essential requirement.”

Scalability and Adaptability: The cloud provides the scalability needed to handle fluctuating workloads and the flexibility to quickly adapt to changing business needs. This is particularly crucial for AGS Health, which supports a large and growing client base.

Cost Optimization: Cloud solutions can significantly reduce IT infrastructure costs, freeing up resources for innovation. Thatapudi notes that the shift to the cloud has allowed AGS Health to invest more in artificial intelligence (AI) and machine learning (ML) initiatives.

Leveraging AI and Machine Learning for Improved Outcomes

AI in healthcare is no longer a futuristic concept; it’s a present-day reality.AGS Health is actively exploring and implementing AI and ML solutions to improve revenue cycle management and clinical documentation.

Predictive Analytics: AI-powered predictive analytics can identify potential coding errors and denials, allowing for proactive intervention. This reduces claim rejections and improves revenue capture.

natural Language Processing (NLP): NLP is used to extract key information from unstructured clinical documentation, improving the accuracy and completeness of coding. This is crucial for clinical documentation improvement (CDI).

Automation of Routine Tasks: AI and ML can automate repetitive tasks, freeing up staff to focus on more complex and value-added activities. This improves efficiency and reduces the risk of human error.

Data analytics: The Cornerstone of Strategic Decision-Making

Healthcare data analytics is central to AGS Health’s strategy. Thatapudi explains how they use data to drive better decision-making.

Real-time Dashboards: AGS Health provides clients with real-time dashboards that track key performance indicators (kpis), such as denial rates, coding accuracy, and revenue cycle performance.

Data Visualization: Effective data visualization tools help clients quickly identify trends and patterns in their data.

Benchmarking: AGS Health benchmarks its clients’ performance against industry standards, providing valuable insights into areas for improvement. This supports performance improvement in healthcare.

Data Governance: Strong data governance policies are essential to ensure data quality, accuracy, and security. Thatapudi emphasizes the importance of establishing clear data ownership and accountability.

Cybersecurity in a Connected Healthcare Ecosystem

The increasing connectivity of healthcare systems also brings heightened cybersecurity threats. Thatapudi outlines AGS Health’s proactive approach.

Zero Trust Architecture: Implementing a zero-trust security model, assuming no user or device is trusted by default, is a key strategy.

Threat Intelligence: Leveraging threat intelligence feeds to stay ahead of emerging threats.

Employee Training: Regular cybersecurity awareness training for all employees. “Human error is frequently enough the weakest link in the security chain.”

Incident Response Plan: A well-defined incident response plan to quickly and effectively address security breaches.

Future Trends: The Road Ahead for Healthcare IT

Looking ahead, Thatapudi identifies several key trends that will shape the future of healthcare IT.

Telehealth Expansion: