Causal-Constrained Reinforcement Learning for Revenue Cycle Optimization

Causal-Constrained Reinforcement Learning for Revenue Cycle Optimization | 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Article Causal-Constrained Reinforcement Learning for Revenue Cycle Optimization Yunguo Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9465761/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 Healthcare revenue cycle management (RCM) loses billions annually to claim denials, yet existing machine learning approaches treat billing as a prediction problem rather than a decision problemthey predict which claims will be denied but do not optimize the coding actions that cause denials. We formulate billing optimization as a constrained sequential decision-making problem under regulatory constraints, integrating causal inference, constrained offline reinforcement learning, and uncertainty quantification into a unified framework. The central theoretical contribution is a proof that the interventional policy value is identifiable from observational claims data despite latent patient severity (Theorem 1), with explicit assumptions (consistency, positivity, conditional ignorability) and sensitivity analysis for unmeasured confounding. A regret decomposition (Theorem 2) isolates causal estimation error from optimization and constraint approximation errors, providing a diagnostic for performance losses. A finite-sample conformal coverage guarantee (Lemma 1) handles policy-induced covariate shift. Uncertainty quantification is embedded in policy training via reward shaping, not merely as a post-hoc filter. On semi-synthetic data with five baselines, the framework achieves a 36% denial rate reduction with stable revenue, zero constraint violations, and well-calibrated uncertainty. A real-world validation on 25,734 claims episodes (151 CPT codes, 3 payers) confirms scalability and produces statistically significant causal ATE estimates. On real data lacking clinical features, the causal reward does not outperform non-causal constrained RL—consistent with the theory, since identification requires observing the full adjustment set. The framework’s primary value is the identification and regret decomposition machinery that enables principled decision-making under causal uncertainty, with practical revenue gains contingent on the quality of available causal estimates. Medical Informatics Bioinformatics Information Retrieval and Management Artificial Intelligence and Machine Learning Health Economics & Outcomes Research Causal inference Offline reinforcement learning Revenue cycle management Conformal prediction Constrained Markov decision process Healthcare billing optimization 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. 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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-9465761","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625910012,"identity":"8a56409d-0082-4ec6-8198-a421bbf43d6a","order_by":0,"name":"Yunguo Yu","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":"Yunguo","middleName":"","lastName":"Yu","suffix":""}],"badges":[],"createdAt":"2026-04-20 02:26:04","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-9465761/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9465761/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107489520,"identity":"8c4ef1c2-3c76-4c6f-95f3-885ec91a66f9","added_by":"auto","created_at":"2026-04-22 02:47:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":482699,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptCCRLRCMRS.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9465761/v1_covered_e347ae5c-2b8e-4f2c-8b93-0cfb78988d5d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eCausal-Constrained Reinforcement Learning for Revenue Cycle Optimization\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Zyter|TruCare","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":"Causal inference, Offline reinforcement learning, Revenue cycle management, Conformal prediction, Constrained Markov decision process, Healthcare billing optimization","lastPublishedDoi":"10.21203/rs.3.rs-9465761/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9465761/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHealthcare revenue cycle management (RCM) loses billions annually to claim denials, yet existing machine learning approaches treat billing as a prediction problem rather than a decision problemthey predict which claims will be denied but do not optimize the coding actions that cause denials. 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