{"id":341,"date":"2026-09-01T00:24:45","date_gmt":"2026-08-31T19:24:45","guid":{"rendered":"https:\/\/medlinkanalytics.com\/blog\/?p=341"},"modified":"2026-09-01T00:37:32","modified_gmt":"2026-08-31T19:37:32","slug":"ai-in-medical-billing-systems","status":"publish","type":"post","link":"https:\/\/medlinkanalytics.com\/blog\/ai-in-medical-billing-systems\/","title":{"rendered":"AI in Medical Billing"},"content":{"rendered":"<figure class=\"wp-block-post-featured-image\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1122\" height=\"1402\" src=\"https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/AI-in-Medical-Billing.png\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"AI in Medical Billing\" style=\"object-fit:cover;\" srcset=\"https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/AI-in-Medical-Billing.png 1122w, https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/AI-in-Medical-Billing-240x300.png 240w, https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/AI-in-Medical-Billing-819x1024.png 819w, https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/AI-in-Medical-Billing-768x960.png 768w\" sizes=\"(max-width: 1122px) 100vw, 1122px\" \/><\/figure>\n\n\n<p class=\"wp-block-paragraph\"><strong>AI in Medical Billing: How Providers Can Keep Up as Payers Automate Claim Denials<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Artificial intelligence has become a defining force on both sides of the medical claims process in 2026. On the provider side, AI-powered billing tools are cutting administrative work, improving clean claim rates, and speeding up eligibility checks and coding. On the payer side, health insurers including UnitedHealth, Cigna, and Humana are facing active class-action lawsuits alleging their AI systems deny claims, sometimes in batches, with minimal or no individualized physician review, at error rates reported as high as 90% on appeal. For healthcare providers and practices, this dual shift means AI adoption is no longer optional on the billing side, and denial management has become a more urgent, higher-stakes function than it was even two years ago.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Medical billing has quietly become one of the most contested fronts in U.S. healthcare technology. It&#8217;s not just that AI is changing how claims get processed, it&#8217;s that AI is now being used by both providers trying to get paid and payers trying to avoid paying, and the two systems are increasingly working against each other. Understanding both sides of that shift is now a practical necessity for any practice managing its own revenue cycle.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What&#8217;s Actually Changing on the Provider Side<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The provider-side AI story is, for the most part, a genuine efficiency gain. According to the American Medical Association&#8217;s 2026 Physician AI Survey, more than 81% of physicians now report using AI professionally in some capacity, more than double the 38% adoption rate recorded just three years earlier. That shift extends into billing and administrative workflows specifically.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The most mature AI applications in medical billing right now include:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Real-time eligibility verification<\/strong> at check-in, catching coverage issues before a claim is even generated<\/li>\n\n\n\n<li><strong>Automated coding and modifier suggestion<\/strong>, where AI analyzes clinical documentation and proposes ICD-10 and CPT codes for a coder or biller to confirm<\/li>\n\n\n\n<li><strong>Predictive denial scoring<\/strong> before submission, flagging claims likely to be denied so they can be corrected proactively rather than reworked afterward<\/li>\n\n\n\n<li><strong>Automated appeal-letter drafting<\/strong>, reducing the manual effort involved in contesting a denial<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">The reported results vary by vendor and should be read as industry benchmarks rather than guarantees, but the direction is consistent: multiple 2026 industry analyses report clean claim rates improving into the 94\u201398% range with AI-assisted scrubbing, denial rates dropping by an estimated 30\u201350% at early-adopting practices, and days in accounts receivable shrinking by roughly 15\u201325 days. Separately, Tebra&#8217;s 2026 State of the U.S. Medical Billing Industry survey of 190 billing professionals found that 71% of early AI adopters report improved efficiency, though the same survey found 59% of billing companies have not yet adopted any AI tools at all, and 73% are not using robotic process automation. In other words, the technology is proven at the leading edge, but adoption remains uneven, and a meaningful gap is opening between practices that have automated core billing tasks and those that haven&#8217;t.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Payers Are Using AI Too &#8211; And It&#8217;s Now the Subject of Major Litigation<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The less comfortable half of this story involves how health insurers are using AI on the other side of the same claims.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Three of the largest U.S. health insurers, UnitedHealth, Cigna, and Humana, are currently defending class-action lawsuits alleging their AI systems improperly deny medically necessary claims, in some cases without meaningful individualized physician review.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>UnitedHealth and nH Predict.<\/strong> A federal class action in the District of Minnesota (<em>Estate of Gene B. Lokken, et al. v. UnitedHealth Group, Inc.<\/em>) alleges that UnitedHealth&#8217;s naviHealth subsidiary used an AI tool called nH Predict to determine how much post-acute nursing and rehabilitation care Medicare Advantage patients should receive \u2014 and that the tool would sometimes override treating physicians&#8217; recommendations. A 2024 U.S. Senate investigation led by Senator Richard Blumenthal found that UnitedHealth&#8217;s denial rate for this category of post-acute care rose from 10.9% in 2020 to 22.7% in 2022 as the company expanded its use of the tool, with skilled-nursing denials climbing roughly ninefold over that period. The plaintiffs allege that more than 90% of nH Predict-based denials were ultimately reversed on appeal. UnitedHealth has stated that nH Predict is a care-planning guide, not a coverage-determination tool, and that coverage decisions are made based on members&#8217; plan terms and CMS criteria. A federal judge allowed the breach-of-contract and good-faith claims in the case to proceed, and as of early 2026 has ordered UnitedHealth to produce internal documents on the tool dating back to 2017.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Cigna and PXDX.<\/strong> A separate class action in the Eastern District of California accuses Cigna of using an algorithm called PXDX to allow its physicians to deny large batches of claims, reportedly hundreds or thousands at a time, without individually reviewing patient medical records, in what the complaint describes as an average review time of roughly 1.2 seconds per claim. The suit, which draws on a ProPublica investigation into Cigna&#8217;s claims practices, alleges this violated California&#8217;s requirement that a licensed physician conduct an individualized medical necessity review. A judge allowed the case to proceed in March 2025. Cigna has stated that the vast majority of claims reviewed through PXDX were automatically <em>approved<\/em>, not denied, and disputes that the tool constitutes AI.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Humana<\/strong> faces a substantially similar suit in the Western District of Kentucky over its own use of the nH Predict model.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">None of these cases have reached a final verdict as of this writing, and all three insurers dispute the characterization of their tools as automated coverage-denial systems. But the litigation itself, and the discovery it has forced, has already surfaced data, like the Senate-documented rise in denial rates, that is shaping how regulators, providers, and patients think about AI&#8217;s role in coverage decisions.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why This Matters for Providers, Not Just Patients<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">It would be easy to read the payer-side litigation as a patient-advocacy story and stop there. It isn&#8217;t. For a billing office or practice administrator, it has direct operational implications.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">First, <strong>appeal economics remain heavily skewed against patients and providers who don&#8217;t appeal.<\/strong> Reporting on the nH Predict litigation cites appeal reversal rates as high as 90%, and separate industry data on U.S. claims broadly puts the share of denials never appealed by patients at under 1%, and by providers at roughly two-thirds. If a meaningful share of payer-side AI denials are being reversed on appeal at that rate, the practices and billing teams that treat every denial as final are very likely leaving recoverable revenue on the table \u2014 a pattern consistent with what we covered in our earlier piece on medical claim denials.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Second, <strong>payer-side automation appears to be contributing to rising denial rates industry-wide.<\/strong> In Tebra&#8217;s 2026 survey, 46% of billing professionals reported increased denial rates over the prior year, and 67% said they believe payers are now using AI specifically to increase denials. Whether or not that belief is fully accurate in every case, it reflects a shift in how billing teams are approaching payer relationships, with more skepticism toward automated denial reasons and a stronger institutional push toward documentation that can withstand automated review.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Third, <strong>the documentation bar is rising on both sides at once.<\/strong> As payers automate first-pass review, the clinical documentation supporting medical necessity has to be more explicit and more thorough than it needed to be when a human reviewer might infer context a strict algorithm won&#8217;t. This is pushing sophisticated billing operations toward AI-assisted coding and pre-submission scrubbing not just for efficiency, but as a defensive measure against automated payer review on the other end.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Regulatory Attention Is Building, Slowly<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Beyond the courts, this issue is drawing broader regulatory scrutiny. Several state insurance regulators have opened inquiries into AI-based utilization review practices, and the National Association of Insurance Commissioners has issued guidance directing insurers to ensure AI tools used in claims decisions don&#8217;t function as a substitute for required human review of medical necessity. No comprehensive federal rule governing AI use in claims adjudication has been finalized as of mid-2026, leaving much of the current oversight to state-level regulators, existing contract and bad-faith law, and the ongoing litigation itself to establish practical boundaries.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What This Means for Your Practice<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A few concrete takeaways follow directly from where things stand in 2026:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Provider-side AI adoption for billing is now closer to standard practice than early experimentation<\/strong>, particularly for eligibility verification and claim scrubbing, the gap between adopters and non-adopters on denial rates and days-in-AR is real and growing.<\/li>\n\n\n\n<li><strong>Denials should be treated as a starting point for appeal review, not a final answer<\/strong>, especially for claims involving post-acute care, extended treatment, or medical necessity determinations where automated review is more likely to be involved.<\/li>\n\n\n\n<li><strong>Documentation quality matters more than it used to.<\/strong> Clinical notes that clearly and explicitly support medical necessity are more resistant to both human and automated denial review.<\/li>\n\n\n\n<li><strong>Tracking denial reasons by payer and by category<\/strong>, rather than treating denials as a single undifferentiated pile of rework, helps identify whether a specific payer&#8217;s pattern looks consistent with automated batch review versus individualized review, which can inform how aggressively to pursue appeals.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">How MedLink Analytics Helps Providers Navigate This Shift<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Practices don&#8217;t need to build their own AI infrastructure or become experts in payer litigation to respond effectively to this shift, but they do need billing operations built to keep pace with it.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>AI-assisted claim scrubbing and coding accuracy<\/strong> &#8211; reducing the documentation and coding errors that give automated payer review systems an easy reason to deny. See <a href=\"https:\/\/medlinkanalytics.com\/services\/revenue-cycle-management\/medical-billing\">Medical Billing &amp; Claims Processing<\/a>.<\/li>\n\n\n\n<li><strong>Denial Management &amp; Appeals<\/strong> &#8211; treating every denial as a reviewable event rather than a final one, with root &#8211; cause analysis by payer and denial category, and structured appeals for claims that meet the pattern of automated or batch-style review. See <a href=\"https:\/\/medlinkanalytics.com\/services\/revenue-cycle-management\/denial-management\">Denial Management &amp; Appeals<\/a>.<\/li>\n\n\n\n<li><strong>AI Solutions for Healthcare<\/strong> &#8211; practical AI claim validation and billing automation built for provider-side workflows, not experimental pilots. See <a href=\"https:\/\/medlinkanalytics.com\/services\/healthcare-technology-solutions\/ai-solutions\">AI Solutions for Healthcare<\/a>.<\/li>\n\n\n\n<li><strong>Revenue Analytics<\/strong> &#8211; dashboards tracking denial trends by payer, so a practice can see whether a specific insurer&#8217;s denial pattern is shifting in ways worth escalating or appealing more aggressively. See <a href=\"https:\/\/medlinkanalytics.com\/services\/healthcare-technology-solutions\/analytics-reporting\">Healthcare Analytics<\/a>.<\/li>\n\n\n\n<li><strong>Compliance Consulting<\/strong> &#8211; reviewing how payer relationships and appeal processes hold up under current regulatory expectations for medical necessity review. See <a href=\"https:\/\/medlinkanalytics.com\/services\/healthcare-consulting-and-analytics\/compliance-consulting\">Compliance Consulting<\/a>.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">MedLink Analytics does not make coverage determinations on behalf of payers and is not a party to any of the litigation referenced in this article &#8211; this discussion reflects publicly reported facts about ongoing cases, not legal conclusions about their outcome. What MedLink Analytics provides is the billing accuracy, denial-tracking discipline, and appeals infrastructure that let a practice respond effectively regardless of which side of the AI shift a given claim runs into.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Want a clearer picture of how your practice&#8217;s denial patterns compare to current industry benchmarks?<\/strong> <a href=\"https:\/\/medlinkanalytics.com\/contact\">Schedule a complimentary practice analysis<\/a>.<\/p>\n\n\n\n<hr class=\"wp-block-separator has-alpha-channel-opacity\"\/>\n\n\n\n<h2 class=\"wp-block-heading\">References<\/h2>\n\n\n\n<ol class=\"wp-block-list\">\n<li>American Medical Association &#8211; 2026 Physician AI Survey findings on physician AI adoption, as cited in industry AI adoption analyses. <a href=\"https:\/\/www.ama-assn.org\">https:\/\/www.ama-assn.org<\/a><\/li>\n\n\n\n<li>Healthcare Finance News &#8211; <em>Class action lawsuit against UnitedHealth&#8217;s AI claim denials advances<\/em>. <a href=\"https:\/\/www.healthcarefinancenews.com\/news\/class-action-lawsuit-against-unitedhealths-ai-claim-denials-advances\">https:\/\/www.healthcarefinancenews.com\/news\/class-action-lawsuit-against-unitedhealths-ai-claim-denials-advances<\/a><\/li>\n\n\n\n<li>Becker&#8217;s Payer Issues &#8211; <em>Judge orders UnitedHealth to hand over documents in AI coverage denial case<\/em>. <a href=\"https:\/\/www.beckerspayer.com\/legal\/judge-orders-unitedhealth-to-hand-over-broad-discovery-in-ai-coverage-denial-case\/\">https:\/\/www.beckerspayer.com\/legal\/judge-orders-unitedhealth-to-hand-over-broad-discovery-in-ai-coverage-denial-case\/<\/a><\/li>\n\n\n\n<li>CBS News &#8211; <em>UnitedHealth uses faulty AI to deny elderly patients medically necessary coverage, lawsuit claims<\/em>. <a href=\"https:\/\/www.cbsnews.com\/news\/unitedhealth-lawsuit-ai-deny-claims-medicare-advantage-health-insurance-denials\/\">https:\/\/www.cbsnews.com\/news\/unitedhealth-lawsuit-ai-deny-claims-medicare-advantage-health-insurance-denials\/<\/a><\/li>\n\n\n\n<li>ArentFox Schiff &#8211; <em>Health Insurers Sued Over Use of Artificial Intelligence to Deny Medical Claims<\/em>. <a href=\"https:\/\/www.afslaw.com\/perspectives\/health-care-counsel-blog\/health-insurers-sued-over-use-artificial-intelligence-deny\">https:\/\/www.afslaw.com\/perspectives\/health-care-counsel-blog\/health-insurers-sued-over-use-artificial-intelligence-deny<\/a><\/li>\n\n\n\n<li>Tressler LLP &#8211; <em>Estate of Gene B. Lokken, et al. v. UnitedHealth Group, Inc. \u2014 AI Risks in Medical Insurance Coverage Disputes<\/em>. <a href=\"https:\/\/www.tresslerllp.com\/thought-leadership\/estate-of-gene-b-lokken-et-al-v-unitedhealth-group-inc-ai-risks-in-medical-insurance-coverage-disputes\/\">https:\/\/www.tresslerllp.com\/thought-leadership\/estate-of-gene-b-lokken-et-al-v-unitedhealth-group-inc-ai-risks-in-medical-insurance-coverage-disputes\/<\/a><\/li>\n\n\n\n<li>Forbes &#8211; <em>Insurers Are Being Sued Over AI Their Own Filings Don&#8217;t Mention<\/em>. <a href=\"https:\/\/www.forbes.com\/sites\/daraabasiita\/2026\/06\/09\/insurers-are-being-sued-over-ai-their-own-filings-dont-mention\/\">https:\/\/www.forbes.com\/sites\/daraabasiita\/2026\/06\/09\/insurers-are-being-sued-over-ai-their-own-filings-dont-mention\/<\/a><\/li>\n\n\n\n<li>Tebra &#8211; <em>2026 Medical Billing Benchmark Report: Revenue, Margins &amp; Denials<\/em>. <a href=\"https:\/\/www.tebra.com\/ebook\/bp-bc26-the-state-of-the-us-medical-billing-industry\">https:\/\/www.tebra.com\/ebook\/bp-bc26-the-state-of-the-us-medical-billing-industry<\/a><\/li>\n\n\n\n<li>NEJM AI &#8211; <em>Characterizing the Clinical Adoption of Medical AI Devices through U.S. Insurance Claims<\/em>. <a href=\"https:\/\/ai.nejm.org\/doi\/full\/10.1056\/AIoa2300030\">https:\/\/ai.nejm.org\/doi\/full\/10.1056\/AIoa2300030<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><em>This article summarizes publicly reported litigation, regulatory activity, and industry survey data as of August 2026. Lawsuit allegations described here are allegations only and have not been proven in court; insurers named have disputed the characterization of their tools in public statements. This content does not constitute legal advice, and practices with specific compliance or contractual questions should consult qualified legal counsel.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>AI in Medical Billing: How Providers Can Keep Up as Payers Automate Claim Denials Artificial intelligence has become a defining 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