Underpayment analysis and intelligent claims aging workflow management have become essential investments for healthcare organizations facing mounting revenue pressure.
With Medicare and Medicaid underpayments reaching $130 billion in 2022 and claim denial rates climbing to nearly 12% across the industry, health systems that deploy AI-powered detection and prioritization tools are recovering tens of millions in previously lost revenue—while those that don’t are leaving substantial money on the table.
The financial stakes are staggering. According to the American Hospital Association, Medicare now pays hospitals just 82 cents for every dollar spent on patient care—a record low that resulted in two-thirds of hospitals posting negative Medicare margins in 2022.
Commercial payers aren’t much better. The American Medical Association reports a 19.3% claims-processing error rate among commercial health insurers, and industry analyses suggest providers lose 1% to 11% of net patient revenue annually to underpayments alone.
The Underpayment Crisis Is Accelerating
Healthcare organizations are facing a perfect storm of revenue leakage.
Initial denial rates reached 11.81% in 2024—a 2.4% increase from the prior year—according to Kodiak Solutions benchmarking data. Medicare Advantage plans, now covering more than half of all Medicare beneficiaries, saw denial rates spike 55.7% in 2023, per Premier Inc. research.
Perhaps most telling, 54.3% of denied claims from private payers are ultimately overturned on appeal, suggesting billions of dollars in legitimate reimbursement that payers initially withhold. Yet appealing these denials costs an average of $118 per claim according to MGMA data, creating an administrative burden that many revenue cycle teams cannot sustain.
“Some of our payers have initial denial rates as high as 30–35%, creating a significant administrative burden,” said Michele Cusack, executive vice president and CFO at Northwell Health.
“However, the cost of not responding to denials is even greater than the administrative burden itself, making it essential to have a strong back-end process in place.”
The accounts receivable picture is equally concerning. Kodiak Solutions reports that true AR days increased 5.2% year-over-year in 2024, driven by rising request-for-information denials and slower payer responses.
Industry benchmarks suggest AR over 90 days should represent less than 15–20% of total receivables, yet average performers now hover closer to 36%.
Technology Is Delivering Measurable Returns
Health systems that have invested in underpayment detection and intelligent workflow tools are seeing substantial financial returns. Case studies from major vendors and industry publications document recoveries ranging from millions to tens of millions of dollars:
- CoxHealth, a five-hospital system in Missouri, partnered with a revenue cycle firm to resolve aged third-party receivables and recovered $37.8 million, reducing accounts over 90 days from 39% to 16% of inventory.
- A large regional health system with $1.8 billion in net patient revenue deployed underpayment analysis technology and recovered $45 million over six years, achieving a 99.7% revenue capture rate and reducing underpayment exposure by 45%.
- An AI-enabled analytics program at a Midwestern health system identified $13 million in underpayments that had been missed by a primary vendor focused only on high-dollar claims.
- A single hospital recovered $55 million after implementing advanced contract analytics that identified alternate revenue code logic issues and pharmacy billing discrepancies.
- Using AiClaim’s Underpayment Analysis, a provider discovered a systemic 1.2% underpayment pattern across more than 200,000 claims from a single payer—an issue that traditional manual auditing would have missed entirely. The AI-powered analysis enabled a large-scale bulk appeal that recovered funds the organization hadn’t realized were missing.
McKinsey & Company estimates that automation and AI could eliminate $200 billion to $360 billion in U.S. healthcare spending, with revenue cycle representing a significant opportunity. Health systems using AI effectively could increase margins by 11% to 19% of net patient service revenue.
The ROI timelines are increasingly attractive. Industry data indicates that AI-powered RCM platforms can demonstrate measurable returns within 40 days, with most organizations achieving positive ROI within 12 months. Black Book Research found organizations implementing RCM automation experienced a 27% decrease in cost-to-collect and a 6% increase in net patient revenue.
AI Is Reshaping How Teams Work Claims
The evolution from manual claims management to intelligent workflow automation represents a fundamental shift in revenue cycle operations.
Modern AI systems don’t just flag underpayments—they predict denial patterns before claims are submitted, prioritize work queues based on collection probability and timely filing deadlines, and generate appeal documentation automatically.
“The future will be leaders managing processes through AI agents, rather than just managing people,” said Erin Hodson, vice president of revenue cycle at Inova Health System.
“The idea of team members working alongside an AI co-pilot to expedite or improve the accuracy of their work is fascinating. From a claims perspective, we’ll eventually be able to manage claims in bulk instead of one by one.”
Hodson noted the urgency created by payer adoption of AI:
“We need to use AI to keep up with payers that also use it. The battle of the bots is coming to the provider payment process, and it’s coming sooner than we think.”
According to the HFMA/Guidehouse 2024 Revenue Cycle Management Report, 81% of healthcare organizations expect to integrate AI into denials management within three years. The CAQH Index found that fully automating nine common RCM transactions could save the industry $16.3 billion.
Implementation Requires Strategic Focus
Organizations achieving the strongest results share several characteristics:
- Technology paired with expertise
AI identifies variances, but experienced analysts determine which are worth pursuing and how. - Root cause analysis over one-time recovery
Preventing repeat underpayments is more valuable than chasing isolated dollars. - Strong collaboration across teams
Revenue cycle operations and managed care contracting must work together.
“It’s important to develop a strategy in which old and new accounts have equal attention, while you look ahead to see which payers are on the horizon so you can flag the difficult ones and work them earlier,” said Amy Raymond, SVP of revenue cycle operations at AKASA.
“One of the most effective ways to reduce days in AR is to focus on sending out clean claims. Preventing denial-causing errors from the start will make you more efficient overall.”
Common pitfalls include:
- Inadequate staffing for underpayment analysis
- Reliance on outdated or Excel-based processes
- Ignoring zero-balance accounts where underpayments often hide
- Over-prioritizing only high-dollar claims while missing systemic issues
The Path Forward for Healthcare Finance Leaders
For healthcare CFOs and revenue cycle directors, the evidence is clear: underpayment analysis and intelligent claims workflow management are no longer optional investments—they are essential components of financial sustainability.
With payer denial rates rising, reimbursement timelines lengthening, and labor costs climbing, organizations that deploy AI-powered detection and prioritization tools will capture revenue that others leave behind.
The $130 billion in government underpayments—and billions more in commercial payer discrepancies—represent a massive opportunity. Health systems that act now by implementing robust underpayment detection, deploying intelligent work prioritization, and training teams to work alongside AI will be positioned to protect margins and reinvest in patient care. Those that wait will continue watching legitimate revenue slip through the cracks.
