8 Ways AI Is Closing the Revenue Gap in Claims, Coding, and Documentation

Healthcare organizations face immense pressure to capture all dollars they’ve earned. Revenue leakage can happen throughout an organization’s revenue cycle, whether due to incomplete documentation or coding inconsistencies, claim errors, avoidable denials, or delayed follow-up.

In some cases, the issue isn’t a matter of effort but rather overabundance: Many revenue cycle teams must process massive volumes of data using high degrees of manual intervention within limited periods of time to spot each potential dollar-generating opportunity.

Indeed, there is no shortage of evidence regarding this struggle.

EY analyzed the state of the healthcare revenue cycle and found that as recently as 2022, only 30% of providers reported denying 10% or more of their claims. This number increased to 38% in 2024 and further jumped to 41% in 2025.

But the trend reflects both challenges and opportunities; while organizations may be struggling with these problems, the global market for artificial intelligence (AI)-driven revenue cycle management reached $20.63 billion in value in 2024, growing to a forecasted $70.12 billion in 2030, according to Grand View Research.

This is where AI in revenue cycle management is changing the equation.

With traditional automation, healthcare organizations have been limited by our ability to define specific rules and outcomes. But with AI capabilities, organizations are able to analyze patterns, identify anomalies, prioritize work and more, enabling us to make better decisions for each part of the revenue cycle.

When done right, AI will help link together clinical documentation, coding, claims, denials and financial data, giving hospitals unprecedented visibility into revenue opportunities before they turn into write-offs. Instead of focusing on automation alone, healthcare organizations will now be able to intelligently manage every aspect of the revenue workflow.

8 Practical Ways AI Is Reducing RCM Inefficiencies, Errors, and Revenue Leakage

Here are 8 ways AI can help your health care organization address revenue gaps in areas like claims, coding, and documentation.

1. AI Identifies Revenue Leakage Before Claims Are Submitted

Often revenue leakage isn’t identified until after the claim hits the payer – but many mistakes happen earlier in the process. Things like missing charges, incomplete documentation, incorrect patient information, coding inconsistencies, and other overlooked billable services cause issues downstream.

While traditional claim workflows catch many of these things with rules-based edits, an AI revenue cycle management system will also analyze historical data to find patterns and identify potential links between them. This ability to pick up on subtle correlations that would otherwise elude even the most experienced staff can mean potential financial savings for your practice.

For example, AI can identify:

Frequently missed charges
Unusual coding patterns
Documentation gaps
Services that are commonly undercoded
Claim combinations associated with denials
Provider-specific billing patterns
Payer-specific reimbursement issues

This empowers your revenue cycle team to shift from searching for revenue leakage after payments have gone through to prevention before submissions are made.

Here’s how AI can help you reduce revenue leakage:

AI helps organizations analyze large volumes of clinical, coding, claims and payment data to detect missing revenue opportunities, inconsistencies and high-risk transactions that may lead to underpayment, denial or write-off.

2. AI-Powered Medical Coding Improves Accuracy and Consistency

At the heart of your healthcare organization’s revenue cycle lies medical coding, which plays an important role in claim acceptance, reimbursement, compliance, and more. Professional coders face challenges as coding becomes more complex with multiple code sets, payers’ changing requirements, documentation standards, and annual coding updates to keep up with.

With AI medical coding, coders are supported by analyzing their clinical documentation and identifying relevant ICD-10, CPT and HCPCS codes.

What are some benefits of using AI-powered medical coding?

Here’s how organizations can benefit from AI-powered medical coding through technology:

Extract relevant clinical information
Suggest appropriate codes
Identify missing documentation
Flag potential coding inconsistencies
Compare documentation against coding requirements
Surface potential specificity opportunities
Support coder review and validation

The goal isn’t just replacing manual coding. Rather, think of this enhanced model as AI-assisted coding with human oversight, using artificial intelligence for tasks that require lots of volume (i.e., high-volume analysis) but having certified coders handle those tasks that involve ambiguity, complexity and/or risk.

In fact, there’s already evidence organizations are starting to see this model play out today in how healthcare organizations are staffing their departments.

As highlighted by HFMA in its report on the Bain & Company research, as technology continues to develop ambient documentation and autonomous coding capabilities, coders and documentation specialists will be shifting from doing repetitive tasks associated with reading charts to managing exceptions and focusing more on reviewing complex cases.

But exactly how is AI improving medical coding accuracy? Let’s find out.

Through AI’s ability to analyze clinical documentation with regard to coding patterns, terminology and established coding rules, this technology can improve coding accuracy by identifying relevant codes and highlighting potential errors or gaps in documentation that should be brought forward for human review. This approach helps organizations achieve higher coding consistency while decreasing the level of manual effort needed to complete each chart.

3. AI Connects Clinical Documentation to Revenue

The connection between documentation and revenue shouldn’t need much explanation.

Without complete documentation of all services provided, coders might not have been able to assign the most appropriate code. It could also lead to issues with undercoding, queries, claim delays and/or compliance.

With AI documentation automation, organizations should be better positioned to identify any such gaps further along in our process.

Using AI, healthcare organizations are able to analyze our clinical notes for things that might be missing, conflicting, or insufficent to demonstrate the service provided.

For example, an AI-enabled workflow can flag:

Missing clinical details
Incomplete procedure documentation
Inconsistent terminology
Documentation that may not support the selected code
Potential opportunities for clarification
Missing elements required for specific services

As a result, this creates a feedback loop between documentation and coding. Instead of having documentation problems identified post-coding (after coding) or as part of an audit process, you can address these concerns much closer to the point of care.

How Does AI Improve Clinical Documentation?

In addition to improving efficiency through automation, AI also helps improve quality of care by creating more thorough documentation that captures all aspects of clinical encounters. By extracting relevant data from clinical conversations and notes, AI systems identify gaps or inconsistencies in documentation and support clinicians in capturing all pertinent information in an organized manner.

This improved clinical documentation enhances billing accuracy as well, ensuring greater alignment between what occurred clinically, what was documented (and coded) for reimbursement purposes.

4. AI Makes Claims Processing More Intelligent

Traditional claims automation systems are concerned with performing certain actions, they’re essentially executed sequences of tasks. With artificial intelligence (AI) claims automation, however, there’s a whole other dimension to the process: decision making and prioritization.

Instead of applying the same level of analysis across all claims, an AI system looks at the nature of each one according to their historical outcome and identifies those that may need more attention.

AI claims processing can support activities such as:

Claim validation
Data quality checks
Payer-specific analysis
Error detection
Claim prioritization
Submission optimization
Exception identification
Status monitoring

With this kind of solution, your revenue cycle team will be able to spend less time manually assessing basic claims, and more time focusing on those that are most likely to fail (or have the highest impact on their bottom line).

Here’s how artificial intelligence automates claims processing: Analyze claim data, spot potential errors or risk factors, apply workflow rules, and route claims to the right place for processing either before or after they’ve been submitted.

The difference is important:

Traditional automation: “If X happens, perform Y.” This would be an example of AI-driven automation: “Based on the data, practices will have available and the historical outcomes, X is likely to create a problem, so let’s prioritize Y”

It also allows these workflows to become more proactive.

5. AI Predicts and Prevents Claim Denials

Denials represent some of the most obvious examples of revenue leakage.

Not only does an organization spend money when setting up appointments, verifying patient eligibility, creating documentation, codifying care and preparing claims for submission, but it also incurs additional costs to address denied claims as they come back from payers.

Industry estimates suggest the average cost to rework a denied claim is about $57, and rising. Hospitals nationwide spent nearly $18 billion to overturn these denials in 2025 alone.

To solve this problem with AI, organizations focus on their efforts on understanding the different conditions under which your claims will be more likely to be denied.

With AI being able to analyse this historical denial data for trends related to:

Payers
Providers
Procedures
Diagnosis codes
Locations
Documentation
Authorization requirements
Patient information
Claim characteristics

Then these patterns are used to identify high-risk claims and flag them before they get submitted.

According to Black Book Research’s 2025 evaluation of AI applications in revenue cycle management, 83% of healthcare organizations utilizing AI-driven automation reported reduced claim denials (by at least 10%) within 6 months. And those that had more mature deployments saw even larger impacts, some as much as 30-40% reduction in claim denials!

Can AI Prevent Claim Denials?

Well, yes! But only if you use it effectively. By understanding what leads to claim denials and identifying high-risk ones, AI provides an opportunity for providers to avoid losing money due to denied claims while also saving time and resources from having to resubmit claims manually.

That being said human review is always required for things that require complex or subjective judgement.

Our biggest potential for growth is going from denial recovery to denial prevention.

Instead of asking:

“Why was this claim denied?”

Revenue cycle teams can increasingly ask:

“What can we change before this claim is submitted?”

Even so, adoption still lags the opportunity. Industry benchmarking from Experian Health’s State of Claims survey found that only about 14% of providers currently use AI specifically for denial management, even as more than a third report double-digit denial rates—suggesting significant room for organizations to close the gap between where AI is applied and where the losses are largest.

6. AI Prioritizes the Work That Matters Most

But most revenue cycle teams don’t have unlimited resources.

At any moment there are often thousands upon thousands of things that need doing, claims, accounts, denials, A/R tasks. If you treat them all the same, your high value opportunities might get lost in the shuffle along with your lower impact ones.

AI can help prioritize these sorts of revenue cycle activities by taking into account various factors such as:

Dollar value
Probability of payment
Denial risk
Aging
Payer behavior
Historical recovery rates
Patient balance
Account complexity
Likelihood of successful intervention

Now this opens up possibilities that are much more “intelligent.” For instance, rather than just tackling the oldest A/R first, what if we had an AI assistant that could flag the accounts where intervention would most likely create a positive impact?

This is how AI for healthcare finance goes beyond being merely an operational tool.

“Now it’s more about allocating our limited resources in the revenue cycle space towards these high value opportunities,” she said.

Candice Powers, USA Health chief revenue officer, told HFMA in an interview on the future of the revenue cycle. “I feel like I’m going to look back someday and say ‘This was the most exciting time to be alive’ in the revenue cycle.” She also acknowledged some challenges that come along with such rapid changes. “There’s some pressure on people to adapt quickly.”

7. AI Finds Patterns Across the Entire Revenue Cycle

But revenue leakage doesn’t belong exclusively to one department, sometimes a denial comes down to documentation issues; other times, it’s due to a code used by your providers that has been coded incorrectly; sometimes, payment variances are due to how payers process payments; at times, authorizations might look like a billing issue (which is really just an A/R issue). When these various functions work in silos with no collaboration, pinpointing the actual root cause can be tough.

AI can also help analyze relationships of revenue cycle data and identify patterns between these various phases.

For example:

Documentation > Coding > Claim > Denial > A/R > Payment

The power of AI comes from its ability to analyze all these events together, rather than just individually. It analyze:

What are your biggest documentation gaps that cause denials?
What are the codes you continually get rejected by payers?
Which providers have strange claims patterns?
What payers are associated with certain denial types?
What claims offer the greatest potential for successful claim recoveries?
Where are preventable errors slipping into our workflow?

One of the biggest differentiators between isolated AI medical billing software versus more general AI RCM software is this notion of using the intelligence across the entire revenue cycle, rather than being stuck inside an individual piece of software.

Michael Peterson, Senior Partner with McKinsey observes “what you’ll see as your first real dip into AI in hospitals will be around the back office – A/R follow-up, underpayment recovery, denials, cash posting… It’s just really labor intensive stuff.”

8. AI Turns Revenue Cycle Automation Into Continuous Improvement

While traditional revenue cycle automation strategies have focused on reducing manual efforts, AI allows companies to learn from their revenue cycle outcomes over time.

A traditional automated system may:

Get a denial, categorize it, and route it to your work queue.
Trigger a predefined workflow.
Analyze the denial using AI-enabled workflows.
Compare it to historical patterns.
Identify the likely root cause.
Determine the appropriate next action.
Prioritize accounts based on financial impact.
Identify other similar claims at risk.
Recommend preventive workflow changes.

That creates a continuous improvement loop:

But there’s also lots of opportunity for revenue cycle AI to create value far beyond labor savings alone.

Most organizations still spend the majority of their time fixing things that happen upstream instead of preventing those issues from occurring, this is precisely what a continuous learning loop is supposed to accomplish, as Colleen Hall, senior VP of revenue cycle at Kodiak Solutions explained to us previously.

And while the promise of AI systems processing more transactions continues to be overblown, there are plenty of other ways to use this tech to improve outcomes downstream.

It’s about making our revenue cycle smarter with every single one of your transactions.

Where Can AI Support Revenue Cycle Operations?

Let’s walk through key revenue cycle activities and explore where AI can help reduce manual effort, improve accuracy, and prioritize work. The level of automation can vary by task, with some workflows suitable for full automation and others requiring human review.

Revenue Cycle Task Area	AI Application
Clinical Documentation	Note generation, documentation analysis, gap identification
Medical Coding	Code suggestions, documentation-to-code mapping, coding validation
Claims	Claim validation, risk scoring, error identification
Claims Processing	Automated routing, prioritization, exception handling
Denials	Denial classification, root-cause analysis, prioritization
A/R	Account prioritization and recovery opportunity identification
Payment Posting	Data extraction, reconciliation support, exception identification
Eligibility	Data analysis and workflow prioritization
Reporting	Pattern identification, anomaly detection, predictive insights
Compliance	Identification of potential documentation and coding risks

The Key Question: Where Should AI Take Over in the Revenue Cycle?

Not every revenue cycle task needs full automation. This process involves understanding how much AI engagement is needed on any given task:

Full automation: High-volume, routine tasks that follow set rules and are defined by outcomes; AI takes over entirely.

AI assisted: AI does the initial analysis or produces recommendations that employees may accept or reject after review.

Human-led: Complex tasks that require clinical judgment, critical thinking, or regulatory compliance; human employees remain in charge.

Ultimately, each organizations should analyze individual tasks based on risk, complexity, and volume, or clinical judgment needed, to understand which tasks are safe to remove from their staff members’ workload while humans remain in control of decisions.

Healthcare organizations who adopt AI technologies across their revenue cycle workflows stand to see many potential benefits. Research from McKinsey estimates that using AI on all parts of the revenue cycle could lead to reductions between 30-60 percent in costs to collect, along with accelerated realizations of cash.

Such savings are at the top of mind for Nikki Harper, chair of revenue cycle for Mayo Clinic. “I tell everyone I speak with, ‘You know we have to decrease our cost to collect,’ and the conversation tends to revolve around the role of technology in accomplishing this.”

Fewer preventable errors
AI can also spot inconsistencies that could later occur during other downstream processes.
Better coding accuracy
AI-assisted coding can help identify relevant codes and documentation gaps.
Earlier denial prevention
Predictive analysis can highlight claims that are similar to historical denials, helping identify potential issues before submission.
Faster claims processing
Automation reduces manual intervention in repetitive workflows.
Better documentation
AI helps providers capture and structure relevant clinical information.
Smarter A/R prioritization
Revenue teams can focus on accounts with higher potential for recovery.
Reduced administrative workload
AI can take over repetitive data analysis and workflow tasks.
Greater revenue visibility
AI can connect patterns across claims, coding, documentation, denials, and payments. The real value is not simply reducing the cost of billing, but understanding how clinical activity, documentation, coding, reimbursement, and cash collection relate to one another.

How Can AI Help Healthcare Organizations Recover Lost Revenue?

AI can help recover lost revenue by identifying opportunities that manual processes may overlook.

These opportunities can occur at multiple points:

Before billing: Identify missing documentation, coding issues, or claim risks.

During claims processing: Detect errors and prioritize high-risk claims.

After submission: Identify claims requiring intervention.

During denial management: Analyze denial causes and prioritize recovery opportunities.

During A/R follow-up: Identify accounts with the highest likelihood of successful recovery.

During payment reconciliation: Surface payment discrepancies and potential underpayments.

Prevent leakage → Capture missed revenue → Recover denied revenue → Prevent recurrence

That is the real potential of AI in medical billing.

The Future of AI in Revenue Cycle Management

Here’s how this process might unfold as we go forward, breaking down into 3 broad categories.

Stage 1: Task Automation

Automate repetitive tasks (e.g., eligibility checks, claim scrubbing, payment posting, basic workflow routing) for your organization.

Stage 2: AI-Assisted RCM

The AI starts to predict risk, prioritize work, identify denial patterns, support coding, detect revenue leakage… and more!

Stage 3: Intelligent Revenue Workflows

Revenue cycle systems increasingly are connecting data from documentation to coding to claims to payments to denials.

And AI helps identify what the “next-best-action” should be, while automation executes on that.

There are still humans who will handle all of those things that require exception handling and use their own judgment when making key decisions (this is really where organizations are transitioning from task level automation to workflow level intelligence).

“Sanjiv Baxi, a physician and partner at McKinsey,” said, “You can’t be paralyzed by the concern that technology is moving too fast… if you don’t act now, organizations that wait until the market settles will fall meaningfully behind.”

Choosing the Right AI RCM Software

When it comes to automation, not all organizations are at the same place. When considering AI RCM software, healthcare leaders should be wary of vendors promising a fully fledged AI solution.

A healthcare leader should be asking themselves these questions:

Can it work within our current EHR and billing ecosystem?
Is it built to analyze both clinical and financial data?
Can it support human review and interventions?
Can it explain or demonstrate its decision-making process?
Can it identify sources of lost revenue?
Can it help predict and prevent denials?
Can it help optimize coding and related workflows?
Can it automate repetitive claim-related tasks?
Can it prioritize A/R and denial management?
Can it appropriately address regulatory and security concerns?
Can it help measure financial impact beyond automation?

The best solution will not always be the one with the fanciest AI. The right solution for you should highlight where it can identify and capitalize on revenue-generating opportunities and how it fits into your existing processes.

AI Should Close the Revenue Gap, Not Create Another Workflow

Healthcare organizations do not need another disconnected technology layer. They need intelligence that spans the revenue cycle.

The best application of AI in revenue cycle management is not necessarily about automating, but about connecting information that was previously siloed across documentation, coding, claims, denials, A/R, and payments.

By identifying the risk, and then automating the next step while allowing the experts to handle the exception, organizations can realize a more responsive, effective revenue cycle.

The question is no longer,

“Can we automate this RCM task?”

but rather, “Where are we losing revenue, and can AI help identify and prevent these losses?”

For healthcare organizations looking to reduce revenue leakage, increase coding accuracy, prevent avoidable denials, and generally make their revenue cycle operations more intelligent, that’s the next frontier in healthcare RCM automation.

Frequently Asked Questions

How does revenue cycle management benefit from AI?

AI analyzes clinical, coding, claims, payment, and denial data to identify trends, flag risks, prioritize receivables, automate processes, and improve overall revenue cycle management. All of this contributes to reducing revenue loss while increasing RCM efficiency.

How can AI help prevent medical billing errors?

By analyzing patient data, coding, documentation, claims, and billing trends, AI can identify areas of concern before errors occur while highlighting tasks that may need human assistance.

How can AI help improve medical coding accuracy?

AI medical coding tools can analyze clinical documentation and recommend relevant ICD-10-CM, CPT, and HCPCS Level II codes while highlighting documentation gaps or inconsistencies. However, medical coders should still be responsible for challenging claims and other complex scenarios.

How can AI help prevent claim denials?

AI can analyze denied claims to identify common themes and highlight similar claims in the future. AI can also identify why certain denials are occurring to help determine ways to prevent future claim rejections. Organizations using AI to help manage their denial reduction process have reported denial reductions of 30-40%.

How can AI help automate the claims process?

AI can analyze claim information, detect errors, prioritize receivables, route exceptions, and streamline overall claims processes, reducing the amount of manual work needed for these tasks.

How can AI help improve clinical documentation?

AI documentation tools can help gather and organize clinical information, detect missing information, and help ensure complete and accurate documentation, which can facilitate accurate coding and billing.

What are the benefits of using AI in RCM?

The benefits of using AI include increased efficiency, reduced manual work, improved medical coding accuracy, reduced claim denials before they occur, improved A/R accuracy and prioritization, improved documentation accuracy and completeness, and an increased ability to identify sources of revenue leakage.

How can AI help healthcare organizations recover lost revenue?

By analyzing documentation, coding, claims, denials, A/R, and payment information, AI can help identify revenue recovery opportunities while prioritizing them based on the potential revenue they represent.

What revenue cycle functions can be automated with AI?

Documentation analysis and organization, medical coding, claim validation, claims processing, denial detection, A/R prioritization, payment posting and reconciliation, reporting, anomaly detection, and many other RCM tasks can be automated to reduce manual work and increase efficiency.

How can AI help reduce revenue leakage?

By detecting errors or exceptions that may lead to lost revenue, AI can help reduce revenue leakage. By analyzing documentation, coding, claims, denials, A/R, and payment data, AI can also help identify where revenue is being lost and why.