In the US, revenue-cycle leaders are under unprecedented pressure: hospital operating margins hovered between 1.3 % and 1.8% in 2024, the second-lowest level on record. At the same time, an AKASA/Healthcare Financial Management Association (HFMA) Pulse survey shows that 46 % of U.S. hospitals now deploy AI in RCM to claw back lost dollars and meet tighter regulations. 

In short, AI is moving from pilot project to financial lifeline. This article maps exactly where intelligent automation is paying off—and how organizations can close the gap before rising costs shut the window of opportunity. Let’s take a closer look. 

AI in RCM: Current Landscape and Drivers of Adoption

Recent industry research reveals that the adoption of AI in RCM is far from uniform::

• 72 % use AI for eligibility/benefits verification.
• 64 % apply it to payment estimation, a surge catalysed by the No Surprises Act.

Beyond those entry points, reports suggest that AI can help with charge-capture audits, denial management, and contract modelling—areas where manual work still swallows hours and invites payer rejections.

Regulation amplifies the urgency. Price-transparency rules expose hidden fees. CMS quality programmes penalise avoidable readmissions. Together, they make error-prone paperwork a direct threat to operating cash. 

AI’s value, therefore, lies less in glamorous predictions and more in day-to-day reliability: cleaner claims, faster cash, lower write-offs. 

What Are the Key Impact Areas of AI in RCM?

Now, let us go over how AI in RCM can benefit healthcare providers:

1. Enhanced Coding Accuracy and Documentation

Modern AI coding assistants have dramatically changed how medical coders work, with real results to show for it. These smart systems now read through doctors' notes instantly, offering ICD-10 code suggestions and catching missing information that might cause claim rejections. 

Research demonstrates that AI-driven coding systems have reduced coding errors by up to 35%.

Recently, the Cleveland Clinic and AKASA announced a strategic collaboration to deploy generative AI tools aimed at enhancing efficiency and accuracy in healthcare coding and documentation. The AI-Powered Coding Assistant can process over 100 clinical documents in approximately 1.5 minutes, significantly reducing the time coders spend reviewing documentation.

Action steps healthcare providers can take: 

• Pilot Computer-assisted Coding (CAC) in high-volume, high-revenue departments (e.g., cardiology, radiology).
• Implement an AI-edited / human-audited loop—coders correct edge cases and feed exceptions back to the algorithm.
• Track DRG shifts monthly to uncover silent under-coding and capture full reimbursement.
  
  

2. Streamlined Claims Processing and Prior Authorization

AI's impact is particularly visible in claims processing, where traditional workflows often involve multiple manual touchpoints. The HFMA reports that AI-driven claims processing reduces processing time while simultaneously improving clean claim rates.

Prior authorization—long a source of frustration—has been notably transformed. McKinsey analysis suggests that AI-enabled prior authorization can automate 50 to 75 percent of manual tasks, boosting efficiency, reducing costs, and freeing clinicians at both payers and providers to focus on complex cases and actual care delivery and coordination.

AI-driven technologies also establish specific criteria for smoother initial claims submission, including setting appropriate bill hold days to allow for proper documentation, device, and supply charge capture. Additionally, eligibility verification systems using AI have demonstrated the ability to reduce initial denials and improve clean claim rates.

The transition hasn't been seamless, though, as legacy system integration remains a significant challenge for many organizations implementing comprehensive AI solutions.

Implementation checklist:

• Embed coverage checks at scheduling, not check-in.
• Auto-route “low-risk” claims straight through; divert high-risk ones to veteran billers.
• Refresh scrubber rules within 48 hours of a new payer edit to stay ahead of denial trends.
  
  

3. Operational Efficiency 

Beyond targeted applications, AI delivers broad operational benefits across the revenue cycle. Virtual assistants now handle self-scheduling and routine inquiries that previously consumed staff time. This reduces administrative tasks in organizations with mature implementations.

Automated systems continuously record and store every interaction with patient data in structured formats. EHR audit logs capture who accessed which patient record at what time and the action they performed, as mandated by HIPAA Security Rule requirements. This streamlines audit preparation and improves documentation completeness compared to manual processes.

Perhaps most significantly, AI in RCM demonstrates impressive scalability—the ability to handle volume increases without proportional resource expansion.

AI in RCM: What’s Next

Generative AI is already drafting operative notes and negotiating authorisations in payer-specific language. Healthcare providers can expect these developments soon:

• Explainable AI dashboards that expose which clinical phrases drive risk scores—critical for audit defensibility.
• Ambient documentation technology is showing impressive results with physicians 
• Real-time contract modelling where AI predicts reimbursement under alternate-payment models before charges hit the ledger.

Despite the impressive benefits, healthcare leaders should approach AI implementation with eyes wide open to the risks. For example, biased training data can lead AI systems to incorrectly flag certain patients for collections when they actually qualify for charity care, potentially worsening healthcare disparities. 

Meanwhile, those mysterious "black-box" denial predictions could violate emerging transparency requirements if your team can't explain why specific claims were flagged. 

Takeaway

Cleaner codes, same-day authorizations, predictive denial defense, and virtual assistants together create a revenue cycle that is faster, fairer, and fundamentally more resilient. Organizations that operationalize AI in RCM now can redirect millions from avoidable write-offs to patient care, while easing the administrative burden on their people.

With intelligent automation at its core, HOM combines expert medical coding assistance, real-time eligibility checks, automated claims routing, and predictive analytics. Whether it’s improving first-pass claim rates, cutting authorization delays, or scaling operations without ballooning costs, HOM equips healthcare providers with the exact tools needed to thrive under financial pressure.

To learn more about how we can assist you, contact us.