Today\u2019s payers are not just automating them. They\u2019re strategically pinpointing which claims to deny based on highly sophisticated analytics that predict provider response or perhaps, ideally, lack thereof. Increasingly, insurers are using AI and advanced analytics not just to automate claims decisions, but to predict provider behavior. The question isn\u2019t always \u201cIs this claim payable?\u201d It\u2019s often, \u201cWill this be worth the provider\u2019s effort to appeal this claim?\u201d<\/p>\n
The goal isn\u2019t merely to enforce rules about medical necessity or coding clarity. It is to predict the claims that won\u2019t be appealed or, similarly, where underpayments will go unnoticed. That\u2019s a subtle but profound shift in the reimbursement landscape that many hospitals aren\u2019t prepared to address.<\/p>\n
From rules-based denials to behavior-based strategy<\/p>\n
Payers have always had access to large volumes of claims data. What\u2019s different now are the tools at their disposal to analyze it. Modern, AI-enabled analytics allow payers to look back across years of claims and see patterns in how hospitals respond.<\/p>\n
They know, for example:<\/p>\n
Which denial codes hospitals appeal most (and least).<\/p>\n
Which service lines specifically see low appeals volume due to resource constraints.<\/p>\n
Where underpayments fall below internal review thresholds.\u00a0<\/p>\n
They can see appeal timelines, overturn rates, and even how often providers do not pursue appeals because the reimbursement dollars don\u2019t justify the work.<\/p>\n
Now, when that data is fed into AI-driven decision engines, insurers are able to assign \u201cdenial risk scores\u201d that factor in the likelihood of appeal and historical appeal outcomes, not just clinical or other administrative criteria. In effect, some denials are designed to succeed not because they are accurate, but because they are unlikely to be challenged.<\/p>\n
A denial that has a low chance of being appealed becomes a low-risk decision, even if it\u2019s questionable. A small underpayment that consistently goes unnoticed becomes an easy win. Multiply those decisions across thousands or millions of claims, and the impact adds up quickly. What makes this especially challenging for hospitals is that it doesn\u2019t always show up as a spike in denials. In many cases, denial rates stay flat. Revenue erosion happens incrementally, spread across claims in ways that are hard to spot without deeper analysis.<\/p>\n
Why many denials \u201cwork\u201d even when they\u2019re wrong<\/p>\n
Most revenue cycle teams triage denial workloads based on timely filing deadlines and expected yield. If the cost in staff time and operating budget to appeal one denial is greater than the likely recovery, it makes sound business sense to let it go. According to Becker\u2019s Hospital Review, the cost per denial worked jumped from roughly $44 in 2022 to more than $57 in 2023<\/a> (30%), collectively costing the industry tens of billions.<\/p>\n Payers understand this calculus just as well as providers do. When AI models start to factor in provider capacity and historical appeal patterns, payers can tilt decisions toward cases where the appeal rate is low. It\u2019s not that those denials are always correct. It\u2019s that they are profitable when left unchallenged. In those cases, denials succeed not because they\u2019re valid, but because they\u2019re inconvenient and costly to address.\u00a0<\/p>\n From a payer standpoint, it\u2019s operational optimization. From a provider standpoint, it\u2019s revenue loss that often goes unexplained because no single claim looks significant on its own.<\/p>\n Over time, those \u201cnot worth it\u201d decisions quietly become baked into expected reimbursement, establishing a new status quo.<\/p>\n What hospitals should be paying attention to<\/p>\n As payers increase their use of AI and automation to issue denials faster, at greater scale, and with increasing complexity, hospitals are falling behind. Overwhelmed by volume, constrained by budgets, and lacking the specialized expertise and technology required to consistently overturn denials, traditional approaches to denials management and tracking performance are no longer enough.\u00a0<\/p>\n While automation can improve standardization and speed, studies show many hospitals still struggle with rising denial volumes and the burdens they place on staff and cash flow. According to a recent poll conducted by MGMA, nearly 60% of providers reported year-over-year increases in claim denials<\/a>, with providers collectively spending upwards of $20 billion annually trying to overturn them.<\/p>\n If hospitals are going to stay ahead, revenue cycle leaders must shift from reaction to strategy. Here are key areas of focus:<\/p>\n Which denial categories have the lowest appeal rates, and are they actually valid?<\/p>\n Where do underpayments consistently fall just below review thresholds?<\/p>\n Which payers show patterns of partial payment or \u201csoft denials\u201d that don\u2019t trigger workflows?<\/p>\n How often do hospitals choose not to appeal due to effort, not merit?<\/p>\n These questions aren\u2019t about blaming teams. They\u2019re about understanding where payer behavior and provider capacity intersect and where that intersection creates risk.<\/p>\n Using data to level the playing field<\/p>\n The good news is that providers can use analytics to counter these dynamics, too. Leading organizations are starting to analyze denials and underpayments through different lenses. Not just by reason code, but by cost to appeal, probability of recovery, and true financial impact.<\/p>\n Instead of asking, \u201cWhy was this denied?\u201d they\u2019re asking:\u00a0<\/p>\n Why does this denial persist?<\/p>\n What claims are most likely to be underpaid systematically?<\/p>\n Which denials have the highest ratio of overturn to effort?<\/p>\n What payer policies correlate with high rates of low-yield denials?<\/p>\n That shift allows hospitals to identify denial patterns that are strategically designed to go unchallenged and bring those issues forward, whether through targeted appeals, contract discussions, or payer negotiations.<\/p>\n When providers can show data-backed patterns rather than isolated examples, conversations with payers change. What once felt like anecdotal frustration becomes measurable, defensible insight.<\/p>\n The revenue you don\u2019t see is the revenue you lose<\/p>\n Hospitals today operate under unprecedented margin pressure. Even as median health system operating margins reached 1.2% in the last quarter of 2025<\/a>, marking the strongest performance of the year, there remains little room for error. In this environment, the biggest risk isn\u2019t the denial that lands squarely in a work queue, but the one that quietly blends into routine operations and never gets questioned.\u00a0\u00a0<\/p>\n As AI becomes more embedded in payer workflows, revenue loss will increasingly be driven by what goes unnoticed rather than what gets rejected outright. With hospitals investing heavily in their own analytics and automation, now is the right time to broaden the conversation. Not just about speeding up appeals but about understanding payer strategy. Not just about working denials but about seeing the patterns behind them.<\/p>\n Because when decisions are designed to stay below the radar, visibility becomes one of the most valuable tools a provider can have.<\/p>\n Photo: digicomphoto, Getty Images<\/p>\n Paul Havey<\/a> is the Chief Commercial Officer of Revecore<\/a>, a leading provider of complex revenue cycle management solutions for hospital systems nationwide. Paul has spent his career in healthcare revenue cycle leadership, including more than a decade at Change Healthcare (now Optum). Paul holds a bachelor\u2019s in business administration from The College of New Jersey and an MBA in finance and consulting from Wake Forest University.<\/p>\n![\"\"](\"https:\/\/www.europesays.com\/ai\/wp-content\/uploads\/2026\/06\/Paul-Havey-150x150.jpg\")
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