Exploring Internet hospital patient demand patterns from online consultation content using text clustering

Exploring Internet hospital patient demand patterns from online consultation content using text clustering | 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 Exploring Internet hospital patient demand patterns from online consultation content using text clustering Yingchun Liu, Chen Jing, Yuzhe Wang, Chunjie Jin, Tianzhi Yu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7797643/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Internet hospital patient generated questions during online consultations are typically short, semantically incomplete, and expressed in a non-standardized manner, which poses challenges for demand recognition and knowledge discovery. This study aims to identify core categories of patient concerns through text clustering based on real-world online consultation data, and to explore their focal issues and dynamic evolution patterns. The findings are expected to provide empirical evidence for optimizing medical resource allocation and improving service delivery models. Methodology We used patient online consultation texts from an Internet hospital as the study material. A hybrid representation was constructed by combining TF-IDF weighted Word2Vec semantic features with LDA topic features, and clustering was performed using the K-means + + algorithm. The clustering performance was evaluated using the Silhouette Coefficient (SC), Davies–Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). In addition, topic analysis and time-series visualization were applied to reveal the distribution and evolution of patient demand themes. Results The proposed model demonstrated superior performance compared with the baseline models, achieving higher stability and interpretability across the evaluation metrics (SC = 0.5473, DBI = 10773.26, CHI = 0.7908). Based on this framework, six major themes were identified: appointment and registration, doctor inquiry and consultation, examinations and tests, medication and inpatient medical records, fee settlement and insurance, and customer services and account management. Temporal evolution analysis further revealed that these themes exhibited stage-specific fluctuations and seasonal aggregation, highlighting the model’s ability to capture both static structures and dynamic trends in patient needs. Conclusion The multi-feature fusion clustering approach enables a more comprehensive exploration of patients’ concerns in online consultations. It provides empirical evidence for understanding the service landscape of internet hospitals. At the same time, it offers important references for advancing precision service delivery and informing policy development in online healthcare. Text clustering Internet hospital Hot topics Online medical care Topic mining Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 08 Dec, 2025 Reviews received at journal 01 Dec, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 03 Nov, 2025 Reviewers agreed at journal 28 Oct, 2025 Reviewers invited by journal 15 Oct, 2025 Editor invited by journal 13 Oct, 2025 Editor assigned by journal 10 Oct, 2025 Submission checks completed at journal 10 Oct, 2025 First submitted to journal 07 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. 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-7797643","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":535853238,"identity":"ef69275c-b8d3-45cd-ba8f-c8e47d9d9c11","order_by":0,"name":"Yingchun Liu","email":"","orcid":"","institution":"Tianjin University of Traditional Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Yingchun","middleName":"","lastName":"Liu","suffix":""},{"id":535853239,"identity":"62d2c57c-1916-420e-a00f-ff813bfa230c","order_by":1,"name":"Chen Jing","email":"","orcid":"","institution":"Inspur 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clustering","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-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Text clustering, Internet hospital, Hot topics, Online medical care, Topic mining","lastPublishedDoi":"10.21203/rs.3.rs-7797643/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797643/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e Internet hospital patient generated questions during online consultations are typically short, semantically incomplete, and expressed in a non-standardized manner, which poses challenges for demand recognition and knowledge discovery. This study aims to identify core categories of patient concerns through text clustering based on real-world online consultation data, and to explore their focal issues and dynamic evolution patterns. The findings are expected to provide empirical evidence for optimizing medical resource allocation and improving service delivery models.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethodology\u003c/b\u003e We used patient online consultation texts from an Internet hospital as the study material. A hybrid representation was constructed by combining TF-IDF weighted Word2Vec semantic features with LDA topic features, and clustering was performed using the K-means\u0026thinsp;+\u0026thinsp;+\u0026thinsp;algorithm. The clustering performance was evaluated using the Silhouette Coefficient (SC), Davies\u0026ndash;Bouldin Index (DBI), and Calinski-Harabasz Index (CHI). In addition, topic analysis and time-series visualization were applied to reveal the distribution and evolution of patient demand themes.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e The proposed model demonstrated superior performance compared with the baseline models, achieving higher stability and interpretability across the evaluation metrics (SC\u0026thinsp;=\u0026thinsp;0.5473, DBI\u0026thinsp;=\u0026thinsp;10773.26, CHI\u0026thinsp;=\u0026thinsp;0.7908). Based on this framework, six major themes were identified: appointment and registration, doctor inquiry and consultation, examinations and tests, medication and inpatient medical records, fee settlement and insurance, and customer services and account management. Temporal evolution analysis further revealed that these themes exhibited stage-specific fluctuations and seasonal aggregation, highlighting the model\u0026rsquo;s ability to capture both static structures and dynamic trends in patient needs.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e The multi-feature fusion clustering approach enables a more comprehensive exploration of patients\u0026rsquo; concerns in online consultations. It provides empirical evidence for understanding the service landscape of internet hospitals. At the same time, it offers important references for advancing precision service delivery and informing policy development in online healthcare.\u003c/p\u003e","manuscriptTitle":"Exploring Internet hospital patient demand patterns from online consultation content using text clustering","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-30 08:38:48","doi":"10.21203/rs.3.rs-7797643/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-08T11:53:13+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-01T16:15:38+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T23:56:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104585931331765847221451832757567391417","date":"2025-11-03T17:53:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"295294288320178538361408072625359092593","date":"2025-10-28T17:43:03+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-15T08:57:57+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-13T22:55:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-10-10T06:56:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-10T06:52:31+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-10-07T08:41:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"2f80ed5d-630f-4b63-bb1c-ee6628363133","owner":[],"postedDate":"October 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T13:25:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-30 08:38:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7797643","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797643","identity":"rs-7797643","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}