Efficient revenue cycle management (RCM) is a persistent challenge for healthcare organizations. It encompasses patient registration, coding, billing, claims submission, and collections. These processes are complex, labor-intensive, and prone to error. In fact, administrative costs account for over 40% of U.S. hospital expenses, amounting to roughly US$ 160 billion each year on RCM activities. Amid this scale of spending, even a few percentage points of errors or denials translate to billions of dollars in lost revenue.
For example, recent government figures show that Medicare Fee-for-Service had an estimated 7.66% improper payment rate in FY2024, reflecting widespread billing and coding errors. Moreover, CMS notes that the ‘vast majority’ of these improper payments occur because claims lack sufficient documentation. This finding suggests that many payment errors are avoidable with better data capture and review.
In this environment of mounting cost pressure and regulatory scrutiny, providers face complex payer relationships and economic pressures. So, AI revenue cycle management is now essential for effective billing operations.
Streamlining Billing and Coding
AI technologies can automate routine billing and coding tasks, freeing staff for more complex work. NLP and machine learning can automatically assign diagnosis and procedure codes from clinical documents. This cuts down on manual work and mistakes.
Vendors describe AI-powered billing modules that validate claims before submission. For example, AGS Health offers an AI platform with dedicated modules for revenue cycle automation and computer-assisted coding. Its revenue cycle automation tools aim to optimize billing and collections processes, prevent denials, and automate tedious, time-consuming tasks. Its computer-assisted coding suite is designed to increase coder productivity while reducing denials, missed charges, and low risk scores.
In practice, these tools cross-check documentation against coding rules in real time. Similarly, FinThrive highlights that its agentic AI can flag incomplete documentation and apply real-time coding corrections. Such automation can catch common errors (like missing modifiers or mismatched codes) before claims go out. The result is cleaner claims on first submission, which reduces rework.
AI-driven billing systems make workflows easier. They remove manual data entry and use consistent rules. Automating code assignment and claims checks boosts accuracy. It also lessens staff workload, which increases operational efficiency.
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Minimizing Claim Denials
Claim denials are a major revenue risk for providers. Each denial often requires costly appeals or corrections, delaying payment. AI can help minimize denials in two ways: by preventing errors that lead to denials, and by quickly identifying denial trends for corrective action. Advanced analytics and automation tools can detect patterns that typically trigger rejections. In June 2025, FinThrive introduced a Denials and Underpayments Analyzer powered by AI. This tool takes raw payer data and turns it into actionable insights, pinpointing denial patterns, underpayment trends, and high-value recovery opportunities. It flags which services or payer relationships are most likely to be underpaid or denied so that staff can focus appeals efforts where they matter most.
Leading RCM firms emphasize that AI-led denials management improves financial outcomes. R1 states that automating tasks such as coding, billing, and denials management increases efficiency. It also enhances accuracy and improves cash flow. These abilities lead to fewer rejected claims and more revenue collected. As denials drop, providers capture more of the revenue they bill and avoid the administrative burden of lengthy appeals.
Accelerating Reimbursements
Fewer errors and denials directly translate into faster payments. When claims are accurate on the first try, payers process them without delay. AI also helps prioritize collections by predicting which accounts are most likely to pay quickly. Industry sources describe dramatic gains in throughput. R1 notes that its AI initiatives will unlock faster, more precise, scalable reimbursement outcomes.
This means providers receive payments sooner and more reliably. FinThrive similarly emphasizes faster revenue flow; its agentic AI capabilities enable providers to recover revenue faster, reduce operational friction, and adapt to payer behavior in real time. By embedding intelligence across the revenue lifecycle, the system helps organizations operate more efficiently [and] recover revenue faster.
In other words, digital assistants can automatically route the right claims, follow up on unpaid accounts, and expedite routine tasks like eligibility checks and prior authorization. Even small percentage improvements in days in accounts receivable can significantly boost cash flow. While specific results vary, vendors claim that clients see measurable performance gains from these technologies. In sum, by reducing the cycle time on each claim and account, AI accelerates overall revenue collection and improves cash flow predictability for healthcare systems.
Enhancing Financial Performance
The cumulative effect of AI in RCM is stronger financial performance. By eliminating waste and capturing revenue more effectively, healthcare organizations improve net margins and free up resources for care delivery. For example, FinThrive reports that its technology helps healthcare organizations increase revenue, reduce costs, [and] expand cash collections across the revenue cycle. The platform aims to capture revenue that might otherwise be lost due to denials or underpayments. It also helps reduce administrative costs.
These improvements align with regulatory goals as well. The U.S. Centers for Medicare & Medicaid Services (CMS) has reported that billions are lost annually to improper payments (e.g. US$ 31.7 billion in Medicare FFS for 2024), much of which stems from documentation gaps. By integrating AI into RCM, providers help ensure compliance with coding and billing requirements, thereby reducing waste.
Overall, industry leaders characterize AI-augmented RCM as a transformative investment. R1’s CEO envisions an ‘AI-native’ revenue cycle that delivers a ‘faster, frictionless, and more transparent financial experience’ for providers and patients. Cognizant, in its TriZetto business, is embedding AI to offload tasks like claims adjudication and prior authorization so teams can focus on patient care.
These technologies close the gap between services rendered and payment received. When denials are minimized and approvals accelerated, organizations need fewer write-offs or bad-debt provisions. Executives can then redirect budget to clinical priorities rather than chasing paperwork.
AI revenue cycle management has the potential to raise net revenue and lower costs simultaneously. For healthcare leaders facing thin margins and evolving payer rules, these tools offer measurable gains.