Governing AI-Driven Prior Authorization:The Economic Case for Symbolic VerificationUnder CMS-0057-F

Governing AI-Driven Prior Authorization:The Economic Case for Symbolic VerificationUnder CMS-0057-F | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Governing AI-Driven Prior Authorization:The Economic Case for Symbolic VerificationUnder CMS-0057-F Yunguo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9672878/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Clinical artificial intelligence systems are transitioning from predictive tools that generate diagnostic outputs for human interpretation to agentic systems capable of autonomous multistep action within clinical workflows, including ordering laboratory tests, initiating medication reconciliation, and updating patient records. Existing trust frameworks, designed for advisory systems and built on output verification and confidence calibration, do not address the governance requirements of autonomous action. We identify an agency gap: the structural mismatch between validated predictions and unvalidated action policies. Using a partially observable constrained decision process (PO-CDP) formalism, we establish the principle of agency nontransferability, demonstrating that trust calibrated at the diagnostic level does not imply safe or appropriate action policies under real-world clinical, institutional, and legal constraints. To address this gap, we propose a three-layer governance stack—epistemic soundness, policy safety, and institutional traceability—that provides verifiable guarantees at each stage of the agentic decision pipeline. This paper presents a theoretical governance framework; the phasespecific milestones in the backcasting roadmap define the empirical validation agenda for each deployment stage. A compositional risk analysis formally predicts that individually safe components can produce unsafe system-level behavior through nonlinear error propagation. An extended backcasting roadmap defines three empirically testable phases for the transition to governed agentic systems: sandboxed action proposals (2027–2029), credentialed policy systems (2030–2032), and supervised autonomy (2033–2035). The transition to agentic clinical AI constitutes a paradigm shift from prediction correctness to policy safety under constraint, requiring institutional design rather than technical improvement alone. Health Economics & Outcomes Research Artificial Intelligence and Machine Learning agentic AI clinical decision support constrained decision processes AI governance agency non-transferability policy admissibility compositional risk autonomy levels patient safety Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9672878","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":637780268,"identity":"d3b9de43-cf24-487d-99d2-f10322684f48","order_by":0,"name":"Yunguo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAw0lEQVRIiWNgGAWjYDADfhCRUECKFskGkBYDUrQYHACTxKi8kWMmXfDrsJzx+dWJHx4YMMjzix0gQsvMvsPGZjfebpYAOsxw5uwEIrTw9hxO3Hbj7AaQlgSD28Rq2Tzj7OYfxGvh+XE4cQN/7zbibJE886zYmrch3VjiBu82iwQDCcJ+4TuevPE2zx9rOf7+s5tv/qiwkeeXJqBF4QCHAQNjG5AlAVYpgV85CMg3sD9gYPgDZPEfIKx6FIyCUTAKRiYAAAlWRzOowwZ5AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0006-2003-3813","institution":"Zyter|TruCare","correspondingAuthor":true,"prefix":"","firstName":"","middleName":"","lastName":"Yunguo","suffix":""}],"badges":[],"createdAt":"2026-05-10 21:54:38","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9672878/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9672878/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109039549,"identity":"365a58d8-4619-4c59-9516-623009cff947","added_by":"auto","created_at":"2026-05-12 03:29:14","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1391808,"visible":true,"origin":"","legend":"","description":"","filename":"manuscriptresearchsquare.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9672878/v1_covered_3ebab1af-ebf7-41a2-81e5-2850c041d8cb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eGoverning AI-Driven Prior Authorization:The Economic Case for Symbolic VerificationUnder CMS-0057-F\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"agentic AI, clinical decision support, constrained decision processes, AI governance, agency non-transferability, policy admissibility, compositional risk, autonomy levels, patient safety","lastPublishedDoi":"10.21203/rs.3.rs-9672878/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9672878/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eClinical artificial intelligence systems are transitioning from predictive tools that generate diagnostic outputs for human interpretation to agentic systems capable of autonomous multistep action within clinical workflows, including ordering laboratory tests, initiating medication reconciliation, and updating patient records. 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A compositional risk analysis formally predicts that individually safe components can produce unsafe system-level behavior through nonlinear error propagation. An extended backcasting roadmap defines three empirically testable phases for the transition to governed agentic systems: sandboxed action proposals (2027–2029), credentialed policy systems (2030–2032), and supervised autonomy (2033–2035). 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