{"id":355,"date":"2026-09-12T03:23:14","date_gmt":"2026-09-11T22:23:14","guid":{"rendered":"https:\/\/medlinkanalytics.com\/blog\/?p=355"},"modified":"2026-09-12T03:23:15","modified_gmt":"2026-09-11T22:23:15","slug":"adjudication-in-medical-billing","status":"publish","type":"post","link":"https:\/\/medlinkanalytics.com\/blog\/adjudication-in-medical-billing\/","title":{"rendered":"Adjudication in Medical Billing"},"content":{"rendered":"<figure class=\"wp-block-post-featured-image\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1080\" height=\"1350\" src=\"https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/Adjudication.png\" class=\"attachment-post-thumbnail size-post-thumbnail wp-post-image\" alt=\"Adjudication in Medical Billing\" style=\"object-fit:cover;\" srcset=\"https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/Adjudication.png 1080w, https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/Adjudication-240x300.png 240w, https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/Adjudication-819x1024.png 819w, https:\/\/medlinkanalytics.com\/blog\/wp-content\/uploads\/2026\/09\/Adjudication-768x960.png 768w\" sizes=\"(max-width: 1080px) 100vw, 1080px\" \/><\/figure>\n\n\n<p class=\"wp-block-paragraph\"><strong>Adjudication in Medical Billing: What Actually Happens Inside the Black Box &#8211; and Why It&#8217;s Under New Scrutiny in 2026<\/strong><\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Adjudication is the process a health plan uses to evaluate a submitted medical claim and decide what it will pay, in what amount, and to whom , checking patient eligibility, code accuracy, medical necessity, contract terms, and payer policy before issuing payment, partial payment, or denial. In 2026, the majority of that process is automated: leading health systems now see auto-adjudication rates approaching 90%, and the CAQH Index , the healthcare industry&#8217;s authoritative benchmark on administrative automation, developed with AHIP, the AMA, and the AHA , found that electronic transactions avoided an estimated $258 billion in U.S. administrative costs in 2024 alone, with a further $18.7 billion in savings still available through deeper automation. But adjudication is not one uniform process , it&#8217;s a layered system where fully automatable, rules-based checks (eligibility, duplicate detection, code validity) coexist with judgment-based determinations (medical necessity, complex coordination of benefits) that regulators and lawmakers are now moving to keep out of AI&#8217;s hands entirely. Understanding that distinction is the difference between a surface-level definition of adjudication and an operationally useful one.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Most explanations of medical claim adjudication stop at the flowchart: claim submitted, claim reviewed, claim paid or denied. That&#8217;s accurate as far as it goes, but it treats adjudication as a black box , a single decision point rather than what it actually is: a layered system combining deterministic, rules-based automation with judgment-based review, running on a technical backbone most providers never see, and increasingly the specific point where healthcare&#8217;s biggest current regulatory fight , the role of AI in coverage decisions , is actually playing out.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What Adjudication Actually Is, Technically<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Adjudication is the formal process by which a health plan evaluates a submitted claim against a set of eligibility, coding, contractual, and policy criteria to determine its payment disposition. It sits at a specific point in the administrative transaction chain defined by HIPAA&#8217;s electronic data interchange (EDI) standards, maintained by the ASC X12 standards body and mandated by CMS for nearly all provider-payer claim transactions: a claim arrives from the provider as an EDI 837 transaction, the payer&#8217;s adjudication engine evaluates it, and the outcome is returned to the provider as an EDI 835 \u2014 the electronic remittance advice \u2014 alongside an Explanation of Benefits sent to the patient.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">What happens between those two transactions is where the substance lives, and it happens in layers rather than as a single check:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Eligibility verification<\/strong> \u2014 confirming the patient was covered under the specific plan on the specific date of service<\/li>\n\n\n\n<li><strong>Administrative and technical edits<\/strong> \u2014 checking for missing data, invalid provider identifiers, duplicate submissions, and formatting errors against payer-specific &#8220;clean claim&#8221; requirements<\/li>\n\n\n\n<li><strong>Code validation<\/strong> \u2014 confirming CPT, HCPCS, and ICD-10 codes are valid, current, and used in permitted combinations, including checks against National Correct Coding Initiative (NCCI) edits that flag improper code pairings<\/li>\n\n\n\n<li><strong>Contract and fee schedule application<\/strong> \u2014 matching the billed service against the provider&#8217;s specific negotiated rate or the applicable fee schedule<\/li>\n\n\n\n<li><strong>Medical necessity and policy review<\/strong> \u2014 evaluating whether the documented service meets the payer&#8217;s coverage policy for that diagnosis and treatment combination<\/li>\n\n\n\n<li><strong>Coordination of benefits<\/strong> \u2014 determining payment responsibility when a patient has more than one applicable insurance plan<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">Most of these layers are genuinely mechanical \u2014 a code either exists in the current code set or it doesn&#8217;t; a patient either had active coverage on the date of service or didn&#8217;t. A smaller subset \u2014 chiefly medical necessity determination and complex coordination-of-benefits scenarios \u2014 require actual judgment about clinical appropriateness. That distinction, not the adjudication process as a whole, is what&#8217;s become the center of policy attention in 2026.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Economics of Adjudication: What the CAQH Index Actually Shows<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">The most authoritative, current data on how adjudication functions at scale comes from the CAQH Index, an annual benchmarking report developed with participation from America&#8217;s Health Insurance Plans, the American Medical Association, and the American Hospital Association, drawing on data from roughly 600 provider organizations and health plans covering 63% of insured Americans. The 2025 edition of the Index \u2014 covering 2024 data \u2014 found that electronic transactions across the healthcare administrative workflow avoided an estimated $258 billion in costs, a 17% increase in automation-driven cost avoidance over the prior year, alongside a 9% reduction in medical administrative spending overall.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">That progress is real, but it&#8217;s uneven, and the unevenness is the more analytically interesting part. Electronic claim status inquiries now reach 81% adoption and electronic claim payments 78% \u2014 both mature, largely solved problems. But electronic attachment adoption \u2014 the digital submission of supporting documentation like medical records or itemized statements, which is frequently what&#8217;s needed to resolve an exception claim \u2014 actually <em>declined<\/em>, from 32% to 24%, according to the 2025 Index as reported by the American Journal of Managed Care. That&#8217;s a genuinely counterintuitive data point: at the exact moment automation is accelerating everywhere else in the claims lifecycle, the specific transaction type needed to resolve the <em>hardest<\/em> claims is moving backward.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This matters because of how adjudication actually splits operationally. The bulk of claims \u2014 clean claims with no missing data, no coding ambiguity, and no coverage questions \u2014 move through automated adjudication with minimal cost, commonly cited at cents on the dollar. The CAQH Index and related industry analyses put the recommended auto-adjudication rate at 85% or higher to maintain a healthy medical-loss ratio, and leading health systems in 2026 are reportedly pushing toward auto-adjudication rates near 90%. But the remaining exception claims \u2014 the ones missing an attachment, flagged for coordination-of-benefits conflict, or requiring manual medical necessity review \u2014 cost dramatically more. Industry estimates place the cost of a manually adjudicated claim at roughly $12 or more, against a fraction of that for a clean, automatically adjudicated one. One industry analysis found that health plans gain the equivalent of five full-time claims analysts&#8217; worth of capacity for every one-percentage-point improvement in their auto-adjudication rate \u2014 a concrete illustration of how disproportionately expensive the exception-handling tail of adjudication really is.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The practical implication for providers is direct: a clean claim isn&#8217;t just faster to get paid \u2014 it&#8217;s dramatically cheaper for the payer to process, which is precisely the category of claim payers have the least administrative incentive to delay or scrutinize. A claim that lands in the exception queue, by contrast, enters exactly the slower, costlier, more judgment-dependent pathway where both delay and denial risk concentrate.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">The Newer Risk: Provider Data Quality Is Becoming an Adjudication Gatekeeper<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A less-discussed but increasingly consequential trend is how payers are tightening data requirements directly inside the adjudication engine itself, rather than handling data problems as a downstream exception. UnitedHealthcare, for example, began enforcing strict NPI and taxonomy code validation across its New York Medicaid network in 2024, and Optum Behavioral Health extended similar NPI and taxonomy enforcement to commercial ABA claims beginning in 2026. These aren&#8217;t isolated administrative tweaks \u2014 they reflect a broader pattern of payers building data-quality enforcement directly into automated adjudication logic, rather than routing imperfect submissions to manual review as they once did.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The effect is that provider data accuracy \u2014 correct NPI numbers, current taxonomy codes, accurate provider-to-service mapping \u2014 has quietly become a precondition for a claim even reaching automated adjudication successfully, rather than a soft factor that manual reviewers could work around. Practices with outdated credentialing or provider data records are increasingly likely to see claims kicked into the more expensive, slower exception pathway \u2014 not because of anything wrong with the clinical service billed, but because the underlying provider data didn&#8217;t meet the adjudication engine&#8217;s validation threshold. This creates a direct, often underappreciated link between credentialing data hygiene and claims adjudication speed.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Why Adjudication Is Now a Regulatory Flashpoint<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Adjudication&#8217;s medical-necessity layer \u2014 the smaller, judgment-based slice of the overall process \u2014 is where 2026&#8217;s most significant healthcare policy fight is actually concentrated, and understanding adjudication&#8217;s internal structure explains why.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">As auto-adjudication rates climb toward 90% in leading systems, an increasing share of that automation inevitably touches judgment-adjacent territory, not just mechanical rule-checking. That&#8217;s precisely the boundary that recent federal and state action has moved to police. The bipartisan Doctors Not AI Act, introduced in Congress on September 1, 2026, would prohibit AI systems from issuing or dictating final adverse benefit determinations involving clinical judgment, requiring a licensed professional&#8217;s independent sign-off on any medical-necessity denial. It follows at least eleven states \u2014 including California, Georgia, Illinois, Washington, Indiana, Alabama, and Louisiana in 2026 alone \u2014 that have already enacted similar requirements. The consistent design pattern across nearly all of this legislation draws exactly the line described above: AI may assist with the mechanical, rules-based layers of adjudication, but a human must own the medical-necessity layer.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">This is a materially more precise way to understand the current AI-in-healthcare-billing debate than &#8220;AI is being used to deny claims,&#8221; which is how the issue is often framed in general coverage. The regulatory response isn&#8217;t targeting adjudication automation broadly \u2014 automating eligibility checks, code validation, and duplicate detection remains uncontroversial and is exactly what the CAQH Index is measuring and encouraging. What&#8217;s being regulated is specifically the medical-necessity determination layer nested inside the broader adjudication process \u2014 the one part of adjudication that was never fully mechanical to begin with.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">Adjudication vs. Reimbursement \u2014 and Why the Distinction Matters More at Scale<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">It&#8217;s worth being precise about a distinction that&#8217;s easy to blur: adjudication is the <em>decision<\/em>, reimbursement is the <em>payment<\/em>. A claim can be fully and correctly adjudicated \u2014 meaning the payer has made a final, accurate determination \u2014 and still experience a reimbursement delay for unrelated reasons, such as payer cash-flow timing or batch payment cycles. Conversely, a claim can be adjudicated quickly but incorrectly, requiring a corrected claim or appeal that restarts part of the adjudication process before reimbursement can occur at all. Understanding adjudication as a discrete decision-making stage \u2014 with its own automation rate, its own cost structure, and now its own regulatory boundary \u2014 rather than as a synonym for &#8220;getting paid,&#8221; is what separates a functional understanding of the revenue cycle from a superficial one.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\">What This Means for Practices and Billing Teams<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">A few concrete, research-grounded takeaways follow from how adjudication actually functions in 2026:<\/p>\n\n\n\n<ol class=\"wp-block-list\">\n<li><strong>Clean claims aren&#8217;t just faster \u2014 they&#8217;re categorically cheaper for the payer to process<\/strong>, which means investment in front-end accuracy (eligibility verification, code validation, complete documentation) pays off disproportionately by keeping claims out of the far more expensive, far slower exception-handling pathway.<\/li>\n\n\n\n<li><strong>Provider data hygiene is now an adjudication variable, not just a credentialing one.<\/strong> Outdated NPI, taxonomy, or provider-mapping data can push an otherwise valid claim into manual review under increasingly strict payer-side validation logic \u2014 making credentialing accuracy directly relevant to claims speed, not just network participation.<\/li>\n\n\n\n<li><strong>Electronic attachment submission remains a genuine bottleneck industry-wide<\/strong>, given its declining adoption rate even as other transaction types automate further \u2014 practices submitting claims that are likely to require supporting documentation should treat attachment completeness as a specific point of failure to manage proactively.<\/li>\n\n\n\n<li><strong>The medical-necessity layer of adjudication is where appeals are most likely to succeed<\/strong>, precisely because it&#8217;s the layer regulators have identified as requiring human judgment \u2014 a useful frame for prioritizing which denials are worth the administrative cost of appealing.<\/li>\n\n\n\n<li><strong>AI-in-adjudication disclosure requirements, where they apply under state law, are a legitimate tool for appeal strategy<\/strong> \u2014 if a denial&#8217;s medical-necessity determination cannot be shown to have received genuine independent clinical review, that&#8217;s an increasingly explicit legal vulnerability for the payer, not just a documentation footnote.<\/li>\n<\/ol>\n\n\n\n<h2 class=\"wp-block-heading\">How MedLink Analytics Supports Practices Through the Adjudication Process<\/h2>\n\n\n\n<p class=\"wp-block-paragraph\">Understanding adjudication&#8217;s internal structure is only useful if it changes how claims are built, submitted, and followed up on \u2014 which is fundamentally a revenue cycle management discipline.<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>Claims scrubbing and coding accuracy<\/strong> \u2014 reducing the eligibility, coding, and data errors that push claims out of automated adjudication and into the far more expensive manual-review pathway. 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>Credentialing and provider data accuracy<\/strong> \u2014 keeping NPI, taxonomy, and provider-mapping data current, given its growing role as a gatekeeper within payers&#8217; automated adjudication logic. See <a href=\"https:\/\/medlinkanalytics.com\/services\/revenue-cycle-management\/credentialing\">Credentialing &amp; Provider Enrollment<\/a>.<\/li>\n\n\n\n<li><strong>Denial Management &amp; Appeals<\/strong> \u2014 targeting appeals strategically at the medical-necessity layer of adjudication, where human-review requirements are strongest and appeal success rates tend to be highest. See <a href=\"https:\/\/medlinkanalytics.com\/services\/revenue-cycle-management\/denial-management\">Denial Management &amp; Appeals<\/a>.<\/li>\n\n\n\n<li><strong>Revenue and performance analytics<\/strong> \u2014 tracking a practice&#8217;s own clean-claim and exception rates over time, identifying which specific data or documentation gaps are driving claims into manual adjudication. See <a href=\"https:\/\/medlinkanalytics.com\/services\/healthcare-technology-solutions\/analytics-reporting\">Healthcare Analytics<\/a>.<\/li>\n<\/ul>\n\n\n\n<p class=\"wp-block-paragraph\">MedLink Analytics does not adjudicate claims or make payer coverage determinations \u2014 those decisions remain with each health plan under its own policies and contractual terms. What MedLink Analytics provides is the claims accuracy, credentialing discipline, and denial-response infrastructure that keep a practice&#8217;s claims moving through the fast, low-cost side of adjudication as often as possible, and equip it to respond effectively when they don&#8217;t.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Want a clearer picture of how much of your practice&#8217;s claims volume is landing in manual review \u2014 and why?<\/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>CAQH \u2014 <em>2025 CAQH Index Shows U.S. Healthcare Avoided $258 Billion and Accelerated Automation, Interoperability and AI Adoption<\/em>. <a href=\"https:\/\/www.caqh.org\">https:\/\/www.caqh.org<\/a><\/li>\n\n\n\n<li>American Journal of Managed Care \u2014 <em>CAQH Index Finds $20 Billion in Cost Savings Opportunities<\/em>. <a href=\"https:\/\/www.ajmc.com\/view\/caqh-index-finds-20-billion-in-cost-savings-opportunities\">https:\/\/www.ajmc.com\/view\/caqh-index-finds-20-billion-in-cost-savings-opportunities<\/a><\/li>\n\n\n\n<li>Centers for Medicare &amp; Medicaid Services \u2014 HIPAA Administrative Simplification, EDI transaction standards (837\/835). <a href=\"https:\/\/www.cms.gov\">https:\/\/www.cms.gov<\/a><\/li>\n\n\n\n<li>ASC X12 \u2014 Health care claim (837) and health care claim payment\/advice (835) transaction standards. <a href=\"https:\/\/x12.org\">https:\/\/x12.org<\/a><\/li>\n\n\n\n<li>Datagence \u2014 <em>Auto-Adjudication Is a Provider Data Problem in Disguise<\/em>. <a href=\"https:\/\/datagence.io\/resources\/auto-adjudication-is-a-provider-data-problem-in-disguise\/\">https:\/\/datagence.io\/resources\/auto-adjudication-is-a-provider-data-problem-in-disguise\/<\/a><\/li>\n\n\n\n<li>OpsDog \u2014 <em>Claims Auto-Adjudication Rate Definition &amp; Benchmark<\/em>. <a href=\"https:\/\/opsdog.com\/products\/claims-auto-adjudication-rate\">https:\/\/opsdog.com\/products\/claims-auto-adjudication-rate<\/a><\/li>\n\n\n\n<li>Office of Rep. Greg Landsman \u2014 <em>Landsman Introduces Bipartisan Legislation to Keep AI from Making Health Care Decisions<\/em> (Doctors Not AI Act, H.R. 10210). <a href=\"https:\/\/landsman.house.gov\/posts\/landsman-introduces-bipartisan-legislation-to-keep-ai-from-making-health-care-decisions\">https:\/\/landsman.house.gov\/posts\/landsman-introduces-bipartisan-legislation-to-keep-ai-from-making-health-care-decisions<\/a><\/li>\n\n\n\n<li>Becker&#8217;s Payer Issues \u2014 <em>7 AI health insurance state laws passed in 2026<\/em>. <a href=\"https:\/\/www.beckerspayer.com\/policy-updates\/7-ai-health-insurance-state-laws-passed-in-2026\/\">https:\/\/www.beckerspayer.com\/policy-updates\/7-ai-health-insurance-state-laws-passed-in-2026\/<\/a><\/li>\n<\/ol>\n\n\n\n<p class=\"wp-block-paragraph\"><em>This article summarizes publicly available industry benchmarking data, technical standards, and regulatory developments as of September 2026. Adjudication rules, payer-specific requirements, and applicable state and federal AI-oversight laws vary and are subject to change; practices should confirm current requirements directly with individual payers and applicable regulatory bodies.<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Adjudication in Medical Billing: What Actually Happens Inside the Black Box &#8211; and Why It&#8217;s Under New Scrutiny in 2026 [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":356,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"site-sidebar-layout":"default","site-content-layout":"","ast-site-content-layout":"default","site-content-style":"default","site-sidebar-style":"default","ast-global-header-display":"","ast-banner-title-visibility":"","ast-main-header-display":"","ast-hfb-above-header-display":"","ast-hfb-below-header-display":"","ast-hfb-mobile-header-display":"","site-post-title":"","ast-breadcrumbs-content":"","ast-featured-img":"","footer-sml-layout":"","ast-disable-related-posts":"","theme-transparent-header-meta":"","adv-header-id-meta":"","stick-header-meta":"","header-above-stick-meta":"","header-main-stick-meta":"","header-below-stick-meta":"","astra-migrate-meta-layouts":"default","ast-page-background-enabled":"default","ast-page-background-meta":{"desktop":{"background-color":"var(--ast-global-color-5)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"ast-content-background-meta":{"desktop":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"tablet":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""},"mobile":{"background-color":"var(--ast-global-color-4)","background-image":"","background-repeat":"repeat","background-position":"center center","background-size":"auto","background-attachment":"scroll","background-type":"","background-media":"","overlay-type":"","overlay-color":"","overlay-opacity":"","overlay-gradient":""}},"footnotes":""},"categories":[1,445,478,479,443,480,477,444,446,449,447,481,448],"tags":[13,24,11,8,16,6,37,22,26],"class_list":["post-355","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog","category-credentialing","category-educational","category-featured","category-healthcare","category-latest","category-medical","category-medicsl-billing","category-medical-coding","category-ranking-and-boosting","category-rcm","category-updated-news","category-virtual-assistance","tag-healthcare-billing-solutions","tag-healthcare-financial-management","tag-medical-billing-and-coding","tag-medical-billing-company","tag-medical-billing-for-doctors","tag-medical-billing-services","tag-medlink-analytics","tag-physician-billing-services","tag-physician-revenue-cycle"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - 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