How to Get Real RCM AI ROI: Beyond the Hype

For those looking to invest in artificial intelligence (AI) within revenue cycle management (RCM), whether you’re a medical billing company or the billing department of a large healthcare provider, measuring RCM AI ROI can be challenging amid all the industry hype. With constant shifts from large language models to agentic AI, separating reality from marketing buzz is increasingly difficult.

Focus on Problems, Not Technology

The best approach to achieving meaningful RCM AI ROI is surprisingly straightforward: ignore the hype and focus on getting things done. As a recent Forbes article titled “Ignore the Noisy AI Hype Machine. Just Get Stuff Done” suggests, practical implementation trumps theoretical possibilities.

Begin by identifying specific problems within your RCM operations that need solving:

• Start with your organization’s most pressing challenges
• Keep an open mind about AI solutions that might address these issues
• Avoid the “solution looking for a problem” approach common in tech

This problem-first approach ensures that any AI implementation directly addresses genuine business needs rather than simply adopting technology for its own sake and generates solid return on investment (ROI).

Establish the Foundation for RCM AI ROI

Before implementing AI, several foundational elements must be in place to ensure measurable RCM AI ROI:

1. Process Documentation and Management

In many cases, process redesign may precede technology implementation. This includes:

• Documenting standard operating procedures (SOPs)
• Implementing project management capabilities
• Creating systems to track and resolve operational issues

As one client company discovered, focusing on these fundamentals before AI implementation created a more fertile ground for technology adoption later.

2. Establish Clear ROI Expectations

Any AI implementation should have defined expectations:

• Projected productivity improvements
• Potential headcount reductions or reallocations
• Expected profitability increases

According to Gartner’s healthcare technology investment research, organizations that establish clear ROI metrics before implementation are 3.2 times more likely to achieve positive returns on their AI investments.

3. Measurement Capabilities

You’ll need both the personnel and systems to:

• Measure baseline performance
• Track improvements post-implementation
• Analyze the resulting data

Without this measurement infrastructure, determining your true RCM AI ROI becomes impossible.

The Critical Role of Data in RCM AI ROI

Perhaps the most crucial factor in achieving positive RCM AI ROI is data quality and accessibility. Consider these requirements:

Data Quality and Integrity

Before implementing AI, ensure your data is reliable by:

• Analyzing data accuracy across systems
• Verifying that reports match underlying data
• Addressing payment posting and other data integrity issues

Many organizations discover significant operational problems during this data validation process, problems that must be resolved before AI can deliver value.

Data Accessibility and Centralization

AI solutions require access to comprehensive data, which presents challenges:

• Practice management and EHR systems may restrict data access
• Organizations often use multiple systems (eClinicalWorks, Epic, etc.)
• Even single systems may have numerous siloed instances (one client had 151 separate instances of the same EHR)

For more on tackling these data challenges, see our guide on EHR data integrity.

A Systematic Approach to RCM AI ROI

Once the foundations are in place, follow this systematic approach to maximize your RCM AI ROI:

1. Identify top operational problems that AI might address
2. Quantify baseline performance for these areas
3. Project expected ROI from AI implementation
4. Run limited tests to validate performance
5. Measure actual results against projections
6. Scale successful implementations across the organization

This approach allows for multiple concurrent projects and even comparison between different vendors’ solutions. According to the Healthcare Financial Management Association, organizations following a structured implementation approach achieve ROI 2-3 times higher than those pursuing ad hoc AI projects.

Beyond Implementation: Measuring Ongoing RCM AI ROI

The ultimate value of AI in revenue cycle management comes from ongoing measurement and optimization. Our RCM analytics solutions can help you track performance metrics and ensure continued ROI from your AI investments.

Conclusion

Achieving meaningful RCM AI ROI isn’t about chasing the latest technological trend. It’s about methodically addressing business problems with appropriate solutions, ensuring data quality and accessibility, and rigorously measuring results. By following this structured approach, healthcare organizations can cut through the hype and realize genuine value from their AI investments.