From Digital Health to Intelligent Health: Redefining Hospital Information Architecture in the Era of Large Language Models

From Digital Health to Intelligent Health: Redefining Hospital Information Architecture in the Era of Large Language Models | 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 From Digital Health to Intelligent Health: Redefining Hospital Information Architecture in the Era of Large Language Models Bing Yan This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9288904/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Background : Traditional Hospital Information Systems (HIS) were designed for digi-tization and data management, but struggle to meet the demands of intelligent health-care in the artificial intelligence era. Objective : This study proposes an AI-native architecture for next-generation hospitalinformation systems and demonstrates its implementation. Methods : We designed a three-layer AI-native architecture comprising intelligent datalayer, AI service layer, and intelligent interaction layer. The architecture was validatedusing OpenClaw, an AI-native workflow platform, to simulate clinical scenarios andevaluate system performance under realistic conditions. Results: Simulation results demonstrate that the proposed architecture achievesmillisecond-level response latency for AI inference, supports real-time processing ofstreaming clinical data, and enables modular deployment of AI services. Performanceprojections based on published benchmarks indicate potential for 40-50% reduction indocumentation time and significant improvements in clinical decision support accuracycompared to traditional HIS. Conclusions : AI-native architecture represents a paradigm shift in hospital informa-tion systems, enabling truly intelligent healthcare delivery. Key challenges include dataprivacy, system integration, and regulatory compliance. Artificial Intelligence Hospital Information System Large Language Models AI-Native Architecture Digital Health Transformation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 May, 2026 Reviewers agreed at journal 18 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor invited by journal 14 Apr, 2026 Editor assigned by journal 10 Apr, 2026 Submission checks completed at journal 10 Apr, 2026 First submitted to journal 01 Apr, 2026 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. 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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-9288904","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635323978,"identity":"a9aec004-9a8f-4004-8437-7b2be8205d48","order_by":0,"name":"Bing Yan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYHACNoYEBgY7fvb2g48ZeA4wMEgQqSVZsudMsjHxWoCAccOMBDNpBgYitBgcP3vswcMdtcwGPAfSqgtk7kTzSzcwfi7Ap+VMXrpB4pnjfObsjcduz+B5ljtzzgFm6Rn4tBzIMZNIbDvGbNlzIO02D8/h3A03EtiYefBpOf8GrIURqNKsmDgtN8C21IC1MBOlRfLGG3ODxLYD4ECWngHUMnNGYrM0Pi1853PMHv5sqwNH5efCnsO5/RLJBz/j06JwAEwdhvAYe8BkAx4NDAzyEOk6KPcHXsWjYBSMglEwQgEAfzdXMAmK/dQAAAAASUVORK5CYII=","orcid":"","institution":"Beijing Hospital","correspondingAuthor":true,"prefix":"","firstName":"Bing","middleName":"","lastName":"Yan","suffix":""}],"badges":[],"createdAt":"2026-04-01 08:25:57","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9288904/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9288904/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108807704,"identity":"247fc075-3711-45e5-83a4-602078a53fb8","added_by":"auto","created_at":"2026-05-08 15:31:16","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":157952,"visible":true,"origin":"","legend":"","description":"","filename":"FinalDraftRevised20260409.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9288904/v1_covered_0f6409c3-1774-4f74-9d19-5f15bf65be3f.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"From Digital Health to Intelligent Health: Redefining Hospital Information Architecture in the Era of Large Language Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"bmc-medical-informatics-and-decision-making","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"midm","sideBox":"Learn more about [BMC Medical Informatics and Decision Making](http://bmcmedinformdecismak.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/midm/default.aspx","title":"BMC Medical Informatics and Decision Making","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Artificial Intelligence, Hospital Information System, Large Language Models, AI-Native Architecture, Digital Health Transformation","lastPublishedDoi":"10.21203/rs.3.rs-9288904/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9288904/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e: Traditional Hospital Information Systems (HIS) were designed for digi-tization and data management, but struggle to meet the demands of intelligent health-care in the artificial intelligence era.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e: This study proposes an AI-native architecture for next-generation hospitalinformation systems and demonstrates its implementation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We designed a three-layer AI-native architecture comprising intelligent datalayer, AI service layer, and intelligent interaction layer. 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