Category: Revenue Cycle Management

For years, hospital finance teams have run revenue cycle on a familiar loop: a claim goes out, a claim comes back denied or underpaid, and staff spend the next several weeks working it back. That loop was manageable when denial rates held fairly steady and reimbursement was reasonably predictable. Neither of those things is true anymore, and the gap between how revenue cycle is typically run and what today’s payer environment actually demands is widening fast.

Denials are climbing, and they are climbing for reasons that have little to do with how hard revenue cycle teams are working. The pressure showing up in the numbers is driven by shifts in payer behavior, patient mix, and administrative complexity that no amount of appeals staffing can fully offset. A reactive model for denials is no longer enough. 

The Reactive Model Is Running Out of Room

The numbers make the case on their own. Kodiak Solutions, which benchmarks revenue cycle performance across more than 2,300 hospitals, found that providers on its platform lost more than $48 billion in 2025 to final denials and uncollected patient balances, a 25% increase over 2024. Clinical denials tied to prior authorization and medical necessity accounted for nearly all of that jump, and initial denial rates have now risen for five straight years.

Experian Health’s 2025 State of Claims survey tells a similar story. Forty-one percent of providers now report denial rates above 10%, a figure that has climbed every year since the survey began in 2022, well past the 5% to 10% range HFMA considers a healthy benchmark.

Payers have grown faster, more automated, and more willing to deny first and let providers make their case on appeal later. Reactive revenue cycle management was built for a world where denials were infrequent, and that world has disappeared.

Complexity Is Coming From Every Direction

Payer mix is a large part of why. Fitch Ratings expects the nonprofit hospital sector to keep shifting toward government payers as the population ages. As more claims run through Medicare and Medicaid, more of a hospital’s revenue becomes subject to rules that change frequently and get enforced unevenly across regions and plans.

At the same time, prior authorization requirements have tightened across commercial and Medicare Advantage plans, and payers are increasingly relying on automated systems to flag claims before a human ever reviews them. Documentation gaps that used to slide through review now get caught immediately, often before a claim leaves the payer’s system.

Increased prior authorization requirements and the shifting payer mix (including more Medicare Advantage plans) multiply the places where a claim can be delayed, denied, or underpaid. A process built to react to one denial at a time will not keep pace. 

The Cost of Fixing Problems After They Happen

Working a denial after the fact is costly. The American Hospital Association estimates that hospitals spent $43 billion in 2025 simply trying to collect payments insurers already owed them for care that had already been delivered. The average hospital now employs roughly 64 administrative and billing staff, about 6.5% of total hospital employment, largely to manage prior authorization, denials, and repeated documentation requests — staff time that goes toward chasing revenue that should have arrived the first time around.

Some of that revenue never comes back at all. Claims that go unappealed or unresubmitted become permanent losses rather than delayed ones. Kodiak’s analysis of its top-performing organizations recommends directing more resources toward preventing clinical denials rather than just appealing them after the fact. That shift in orientation only works if an organization can see the patterns behind its denials rather than just the individual claims in front of it.

What a Proactive Revenue Cycle Actually Looks Like

Proactive revenue cycle management starts when you look at patterns of claim denials vs. the individual claims themselves. That requires analyzing denials and underpayments at the root cause level, tracing a problem back to the payer, the service line, the documentation gap, or the workflow step that keeps generating it, and fixing that step before claims go out the door instead of fighting each one after it comes back.

Most revenue cycle teams were not built to operate this way. Treating denial and underpayment data as a feedback loop into clinical documentation, coding, scheduling, and patient access is a different discipline than working a queue of appeals, and it gives finance leaders visibility into where revenue is at risk before it turns into a write-off, rather than after. It also changes what success looks like. Recovered revenue still matters, but the number worth watching more closely is how much denial volume never happens in the first place.

Revenue Cycle Belongs in the Strategic Conversation

Hospital leaders can control how quickly their organization identifies the root cause of a reimbursement problem and closes it, regardless of demographic shifts, payer mix, or how aggressively insurers adjudicate claims. With costs rising and margins remaining thin across much of the sector, that capability may be the difference between a hospital making or losing money. Health systems that rapidly detect denial patterns and address root causes through operational improvements are the ones most likely to protect their margins over the next several years. These organizations will hold their footing while others spend the next few years chasing revenue they should have collected the first time.

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About Noah Breslow

As CEO of Revecore, Noah Breslow brings more than 20 years of executive leadership experience, with a focus on driving growth, innovation, and transformation in complex, highly regulated industries. Most recently, Breslow was a Partner at Bain Capital Ventures (BCV), where he led their portfolio support team, built out data-driven investment tooling, and helped incubate two startups at the forefront of applying AI in the insurance claims processing and wealth management industries.

Prior to BCV, Breslow served as Chairman and CEO of OnDeck, a pioneering online small business lender, where he built the business from its earliest stages, took it public and ultimately facilitated its acquisition. OnDeck used data and analytics to effectively deliver capital at scale to businesses historically underserved by banks. Earlier in his career, Breslow held leadership roles in product, engineering and marketing. He holds a Bachelor of Science in Computer Science and Engineering from MIT and an MBA from Harvard Business School.

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Healthcare software colleagues of mine often describe a recurring discussion they have with their outsourced development teams. They recount that when they try to explain the difference between a billing diagnosis and a clinical diagnosis, the engineers on the other end deliver a common platitude: “a diagnosis is a diagnosis.” 

That answer is defensible in ordinary English. In healthcare data, however, it is wrong in a way every team that accepts it inherits. The formal name of the “diagnosis” system is the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10). Statisticians created it in 1893 to count causes of death, and around 1938, leaders suggested that the codes might be useful to health insurers. Medicare then tied hospital reimbursement to those codes in 1983, and the rest is history.

Accordingly, I have started referring to ICD-10 as a billing category rather than a diagnosis code. Colleagues tell me the phrasing sounds pedantic, but we have all witnessed what has happened due to this lack of differentiation. Software analysts carry the loose usage into product requirements documents, data dictionaries and integration specifications. Developers then build software that treats a category as though it were a clinical finding. We are all suffering the consequences of asking a statistical instrument to carry the complexity and nuance within clinical meaning.

Grouper or Splitter?

The vocabulary distinction reminds me of when clinical informaticists once asked each other whether they were groupers or splitters. Groupers gather related conditions under a shared heading, while splitters separate them down to the specific entity. The people who built the ICD chose grouping because a statistical classification exists to produce comparable counts across whole populations. Grouping is what makes an epidemiological instrument work, but a clinical one fail.

Conversely, clinical care has continued to split because treatment now turns on distinctions a single code cannot carry. One example is ICD-10 code G60.0, hereditary motor and sensory neuropathy. Coders assign that single category to Charcot-Marie-Tooth disease (CMT) and to its near relatives alike. Yet researchers have now linked more than 100 genes to CMT alone, and dozens of clinically distinct diseases are filed under that heading, each with its own inheritance pattern and treatment path. A claim clears on G60.0 without difficulty, while a physician reading G60.0 learns almost nothing.

Maintainers cannot resolve this by adding codes. They added 487 new diagnosis codes in the fiscal year 2026 update, which all offer important detail. Even so, a larger classification system is still a classification system rather than a clinical vocabulary. 

Where Therapy Already Split

Pharmaceutical manufacturers have already committed to the splitter’s position, a decision their product names record. Anyone watching evening television will hear a run of medications ending in -mab or -nib. Chemists apply the first stem to a monoclonal antibody engineered against a single protein, the second to a small molecule built to inhibit a specific kinase. 

Eventually, however, even these naming conventions were not specific enough. In October 2021, the World Health Organization retired the -mab stem and replaced it with four stems because manufacturers had exhausted the available names. Concurrently, the FDA endorsed greater specificity through new drug approvals. Personalized medicines accounted for roughly 36% of new therapeutic molecular entities in 2025, the sixth consecutive year they exceeded a third of new approvals. Therapy is now chosen at the level of a protein, while the diagnosis pointing toward it is still recorded as a bucket.

Compiling the Confusion

AI-software vendors have supercharged the vocabulary problem operationally through ambient documentation. These solutions listen to a visit, infer a diagnosis and produce an ICD-10 code. That code is then treated as the diagnosis because that is the word everyone uses. Independent testers have measured the best general-purpose models at roughly 50% to 59% accuracy on medical coding. Consequently, a model that cannot determine the specific condition selects the broader code, presenting the clinician with a settled answer that is likely less accurate and certainly less informative.

Unfortunately, many vendors also group the alternate phrasings listed beneath a code as synonyms for it, which might be acceptable for billing, but is misleading for care. Several of those phrasings name distinct diseases with different genetics and different treatment. With only the category recorded, every downstream clinician may need to conduct their own time-consuming and costly investigation to arrive at a real diagnosis.

Writing the Distinction Into Systems

On a macro-level, fixing the problem must involve everyone in healthcare consistently differentiating ICD-10 codes as billing categories, both when speaking and writing. On a micro-level, software analysts should also distinguish the billing category and the clinical diagnosis as separate data elements. Similarly, evaluation teams should ensure a clinically specific diagnosis exists alongside the code captured in a way that a downstream platform can access and interpret it. 

Until then, however, an evidence-based clinical knowledge layer can be integrated into electronic health record systems to import the specific diagnosis in the free text as a computable concept already mapped to its billing category, so both are recorded. 

As a result, reimbursement is unaffected, but, more importantly, the next clinician then sees the clinical explanation alongside the code. So, in the past, when G60.0 (hereditary motor and sensory neuropathy) alone reported almost nothing, the additional detail now helps the clinician understand the type of hereditary neuropathy present to inform their decisions, potentially avoiding repetitive testing or ineffective therapies. The patient then receives the right treatment sooner, one that may never have been pursued if the condition remained obscured behind a vague label.

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About Jay Anders, MD

Dr. Jay Anders serves as the Chief Medical Officer of Medicomp Systems, where he plays a pivotal role in product development and acts as a liaison to the healthcare community. He champions the use of technology to empower clinicians, rather than hinder them.

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