Robust Conversational Framework for Pharmaceutical Monitoring and Domestic Delivery Utilizing Generative AI and FHIR APIs

Robust Conversational Framework for Pharmaceutical Monitoring and Domestic Delivery Utilizing Generative AI and FHIR APIs | 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 Robust Conversational Framework for Pharmaceutical Monitoring and Domestic Delivery Utilizing Generative AI and FHIR APIs Sreenivasa Reddy Hulebeedu Reddy, Yashaswini Sree Krishna This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9109054/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 12 You are reading this latest preprint version Abstract Pharmacy management and home delivery services increasingly rely on conversational interfaces to enhance patient engagement and operational efficiency. Nonetheless, current technologies often lack clinical reliability, system-wide interoperability, and secure integration with healthcare systems. This research presents a safe conversational framework for managing medications and home delivery, integrating generative AI-based clinical dialogue processing with regulated FHIR-compliant outputs. The proposed system integrates clinical conversation capture, medication-related intent classification, feature-driven text appearance, and transformer-inspired predictive modeling to differentiate medication-centric interactions from administrative or non-clinical inquiries. Publicly available clinical discourse datasets are used to ensure repeatability, and verified machine-learning classifiers are used to evaluate system performance. Model assessment employs accuracy, precision, recall, F1-score, ROC analysis, error distribution heatmaps, and additional metrics to evaluate prediction quality and deployment viability. Experimental findings indicate that the proposed system achieves strong classification performance while maintaining low inference latency, enabling real-time conversational applications. Comparative examinations across many models reinforce the approach's stability and generalization. The system's outputs are aligned with FHIR-compatible medication request frameworks, facilitating seamless integration with digital health platforms and home-delivery providers. The findings demonstrate that secure conversational AI, when integrated with standardized clinical interoperability, can efficiently support scalable, reliable drug management in home healthcare environments. Conversational Artificial Intelligence Pharmaceutical Management Clinical Natural Language Processing Fast Healthcare Interoperability Resources APIs Healthcare Interoperability Machine Learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 21 Apr, 2026 Reviews received at journal 18 Apr, 2026 Reviews received at journal 15 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers invited by journal 06 Apr, 2026 Editor assigned by journal 17 Mar, 2026 Submission checks completed at journal 17 Mar, 2026 First submitted to journal 12 Mar, 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. 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-9109054","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":610057126,"identity":"50c63d18-3635-48ca-94c5-9e0d28ffd5b4","order_by":0,"name":"Sreenivasa Reddy Hulebeedu Reddy","email":"data:image/png;base64,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","orcid":"","institution":"Varenya Inc","correspondingAuthor":true,"prefix":"","firstName":"Sreenivasa","middleName":"Reddy Hulebeedu","lastName":"Reddy","suffix":""},{"id":610057136,"identity":"7411f10f-5600-4d0c-a0ae-e75ac6339534","order_by":1,"name":"Yashaswini Sree Krishna","email":"","orcid":"","institution":"Cisco Systems (United States)","correspondingAuthor":false,"prefix":"","firstName":"Yashaswini","middleName":"Sree","lastName":"Krishna","suffix":""}],"badges":[],"createdAt":"2026-03-13 01:23:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9109054/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9109054/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":105563433,"identity":"ec54bfdc-7e08-4cba-8d5f-f2dce01839f9","added_by":"auto","created_at":"2026-03-27 12:47:00","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":519559,"visible":true,"origin":"","legend":"","description":"","filename":"JournalPaperRevised.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9109054/v1_covered_8b316156-0abb-4750-8b3a-90d9601f0259.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Robust Conversational Framework for Pharmaceutical Monitoring and Domestic Delivery Utilizing Generative AI and FHIR APIs","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"discover-artificial-intelligence","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"diai","sideBox":"Learn more about [Discover Artificial Intelligence](https://www.springer.com/44163)","snPcode":"","submissionUrl":"","title":"Discover Artificial Intelligence","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Conversational Artificial Intelligence, Pharmaceutical Management, Clinical Natural Language Processing, Fast Healthcare Interoperability Resources APIs, Healthcare Interoperability, Machine Learning","lastPublishedDoi":"10.21203/rs.3.rs-9109054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9109054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePharmacy management and home delivery services increasingly rely on conversational interfaces to enhance patient engagement and operational efficiency. 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