Cloud vs. On-Premise Large Language Models for Urgent Patient- Portal Message Screening: A Comparative Evaluation

Cloud vs. On-Premise Large Language Models for Urgent Patient- Portal Message Screening: A Comparative Evaluation | 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 Article Cloud vs. On-Premise Large Language Models for Urgent Patient- Portal Message Screening: A Comparative Evaluation Valdery Moura Junior, Susanna Gallani, Lara Basovic, Majed Alomar, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7830207/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Importance Patient portal messaging has become a core feature of outpatient care, particularly in neurology. In epilepsy care, timely triage of urgent symptoms — such as breakthrough seizures or adverse medication effects — and efficient evaluation of urgency level are critical to patient safety. However, increasing message volume and a nationwide neurologist shortage have intensified clinician burden and delayed response times. Large language models (LLMs) may offer a scalable solution. A key step to achieving this goal is to compare performance across cloud-based and locally deployable models and to estimate the impact of the differences in high-stakes clinical contexts. Objective To evaluate the urgency and message-type classification performance of six LLMs - three commercial cloud-hosted (GPT-4o, GPT-5, GPT-5 Mini) and three locally deployable open-weight models (Llama 4 Scout, GPT-OSS 20B, Gemma 3 27B) - against a reference standard in outpatient epilepsy care. Design, Setting, and Participants: Retrospective diagnostic accuracy study of 503 de-identified patient portal messages from adult outpatients at a tertiary epilepsy clinic. Five epilepsy fellowship-trained neurologists independently annotated each message using a standard operating procedure (SOP) with high inter-rater reliability (Fleiss’ κ ≥ 0.80). Analyses were stratified by three non–mutually exclusive levels of physician consensus: Unanimous (5/5), Majority (≥ 3/5), and Any MD Match. Main Outcomes and Measures: Primary outcomes included sensitivity and negative predictive value (NPV) for urgency classification under Unanimous or Majority reference strata. Secondary outcomes included specificity, positive predictive value (PPV), overall accuracy, and message-type classification accuracy. Results Under the Unanimous reference standard, five of six models achieved perfect sensitivity and NPV, indicating safe rule-out performance. Under the Majority consensus, GPT-5 achieved the highest sensitivity (0.98) and NPV (1.00), while GPT-4o and Llama 4 Scout offered balanced performance with strong specificity (0.87–0.88) and NPV (≥ 0.97). GPT-OSS 20B demonstrated high specificity (0.95) but lower sensitivity (0.57), while Gemma 3 27B provided intermediate performance and supports full on-premise deployment. GPT-5 Mini offered a cost-efficient cloud alternative with solid overall performance, though reproducibility was limited by non-configurable decoding. Conclusions and Relevance: In high-risk outpatient neurology, both cloud-hosted and locally deployed LLMs demonstrated screening-level performance comparable to epilepsy fellowship-trained neurologists. Performance trade-offs between sensitivity and specificity allow institutions to tailor model selection to operational goals - whether minimizing false negatives, reducing alert burden, or ensuring Protected Health Information (PHI) containment. These results support the safe, scalable, and privacy-preserving deployment of LLM-powered triage systems across digitally burdened clinical neurology settings. Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Biological sciences/Neuroscience Full Text Additional Declarations No competing interests reported. Supplementary Files AIClassifierSuplement20251015v7.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 04 Mar, 2026 Reviews received at journal 02 Feb, 2026 Reviews received at journal 02 Feb, 2026 Reviews received at journal 22 Dec, 2025 Reviewers agreed at journal 11 Dec, 2025 Reviewers agreed at journal 08 Dec, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers invited by journal 09 Nov, 2025 Editor assigned by journal 18 Oct, 2025 Submission checks completed at journal 18 Oct, 2025 First submitted to journal 10 Oct, 2025 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. 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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-7830207","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":543467437,"identity":"d65db401-2fb6-4a65-90d4-40bd91b63192","order_by":0,"name":"Valdery Moura Junior","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6ElEQVRIiWNgGAWjYPACGwYGCQY2KCeBgYGHsJY00rUcJkGLwbXDx6Qras5Hy0c3sD34ueewvHl7AuODt214tNxOS5M8c+x27sY7B9gNe54dNpxz5gGz4Vw8WiRn55hJNrABtcxIYJPgOZDGOEMigU2al6CWf+fAWiT/HEizB2ph/41PC780UEtj24Hc+SDDeQ7YJIJsYcavJS3ZsrEvOXeDRGKbtMwBm+QZPA+bJeecw62FTTr54M2Gb3a582ckH5N8c0DCdgZ78sEPb8pwa4EDgwOMDVAmnEEAyBOpbhSMglEwCkYgAACaBFCamYgU5QAAAABJRU5ErkJggg==","orcid":"","institution":"Mass General Brigham","correspondingAuthor":true,"prefix":"","firstName":"Valdery","middleName":"Moura","lastName":"Junior","suffix":""},{"id":543467438,"identity":"f2b00afd-c9bc-4f7b-b382-3493c67bb844","order_by":1,"name":"Susanna Gallani","email":"","orcid":"","institution":"Harvard Business School","correspondingAuthor":false,"prefix":"","firstName":"Susanna","middleName":"","lastName":"Gallani","suffix":""},{"id":543467440,"identity":"4f5bc2e0-adfb-49df-bf6f-fca527df0381","order_by":2,"name":"Lara Basovic","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Lara","middleName":"","lastName":"Basovic","suffix":""},{"id":543467441,"identity":"dc8c1f24-c410-4b5b-a543-776efb18cca2","order_by":3,"name":"Majed Alomar","email":"","orcid":"","institution":"Massachusetts General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Majed","middleName":"","lastName":"Alomar","suffix":""},{"id":543467442,"identity":"f8950bcb-1fca-4d5e-b2f2-a74e73b5184b","order_by":4,"name":"Jason C. 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Evaluation","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":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7830207/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7830207/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eImportance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient portal messaging has become a core feature of outpatient care, particularly in neurology. In epilepsy care, timely triage of urgent symptoms — such as breakthrough seizures or adverse medication effects — and efficient evaluation of urgency level are critical to patient safety. However, increasing message volume and a nationwide neurologist shortage have intensified clinician burden and delayed response times. Large language models (LLMs) may offer a scalable solution. A key step to achieving this goal is to compare performance across cloud-based and locally deployable models and to estimate the impact of the differences in high-stakes clinical contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo evaluate the urgency and message-type classification performance of six LLMs - three commercial cloud-hosted (GPT-4o, GPT-5, GPT-5 Mini) and three locally deployable open-weight models (Llama 4 Scout, GPT-OSS 20B, Gemma 3 27B) - against a reference standard in outpatient epilepsy care.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDesign, Setting, and Participants:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRetrospective diagnostic accuracy study of 503 de-identified patient portal messages from adult outpatients at a tertiary epilepsy clinic. Five epilepsy fellowship-trained neurologists independently annotated each message using a standard operating procedure (SOP) with high inter-rater reliability (Fleiss’ κ ≥ 0.80). Analyses were stratified by three non–mutually exclusive levels of physician consensus: Unanimous (5/5), Majority (≥ 3/5), and Any MD Match.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMain Outcomes and Measures:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrimary outcomes included sensitivity and negative predictive value (NPV) for urgency classification under Unanimous or Majority reference strata. Secondary outcomes included specificity, positive predictive value (PPV), overall accuracy, and message-type classification accuracy.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnder the Unanimous reference standard, five of six models achieved perfect sensitivity and NPV, indicating safe rule-out performance. Under the Majority consensus, GPT-5 achieved the highest sensitivity (0.98) and NPV (1.00), while GPT-4o and Llama 4 Scout offered balanced performance with strong specificity (0.87–0.88) and NPV (≥ 0.97). GPT-OSS 20B demonstrated high specificity (0.95) but lower sensitivity (0.57), while Gemma 3 27B provided intermediate performance and supports full on-premise deployment. GPT-5 Mini offered a cost-efficient cloud alternative with solid overall performance, though reproducibility was limited by non-configurable decoding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions and Relevance:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn high-risk outpatient neurology, both cloud-hosted and locally deployed LLMs demonstrated screening-level performance comparable to epilepsy fellowship-trained neurologists. Performance trade-offs between sensitivity and specificity allow institutions to tailor model selection to operational goals - whether minimizing false negatives, reducing alert burden, or ensuring Protected Health Information (PHI) containment. These results support the safe, scalable, and privacy-preserving deployment of LLM-powered triage systems across digitally burdened clinical neurology settings.\u003c/p\u003e","manuscriptTitle":"Cloud vs. On-Premise Large Language Models for Urgent Patient- Portal Message Screening: A Comparative Evaluation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-18 22:43:38","doi":"10.21203/rs.3.rs-7830207/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-04T11:18:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T15:09:14+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-02T13:09:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-22T13:15:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"157278340134315298041589551712935020298","date":"2025-12-11T15:49:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"332490371402168065607823827216610040436","date":"2025-12-08T15:42:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"291209418162658244596673174415367554747","date":"2025-11-11T16:41:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-09T15:16:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-18T20:29:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-18T14:43:16+00:00","index":"","fulltext":""},{"type":"submitted","content":"npj Digital Medicine","date":"2025-10-10T21:18:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"npj-digital-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"npjdigitalmed","sideBox":"Learn more about [npj Digital Medicine](http://www.nature.com/npjdigitalmed/)","snPcode":"41746","submissionUrl":"https://submission.springernature.com/new-submission/41746/3","title":"npj Digital Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"NPJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0711d3d7-d016-4e1c-aeef-fc06c4ea2c6f","owner":[],"postedDate":"November 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":57818047,"name":"Health sciences/Health care"},{"id":57818048,"name":"Health sciences/Medical research"},{"id":57818049,"name":"Health sciences/Neurology"},{"id":57818050,"name":"Biological sciences/Neuroscience"}],"tags":[],"updatedAt":"2026-05-03T21:38:15+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-18 22:43:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7830207","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7830207","identity":"rs-7830207","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}