Early Prediction of Hospital Admission and Specialty for Proactive Bed Allocation in the Emergency Department

Early Prediction of Hospital Admission and Specialty for Proactive Bed Allocation in the Emergency Department | 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 Early Prediction of Hospital Admission and Specialty for Proactive Bed Allocation in the Emergency Department Simon Schiff, Natalie Kohler, Mareike Böckel, Ralf Möller, Sebastian Wolfrum, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8563126/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 Purpose : Emergency department (ED) overcrowding is frequently driven by exit block, where patients with an admission decision wait for an inpatient bed. Early prediction of hospital admission and target specialty department may support proactive bed allocation. Methods : We analyzed approximately 160,000 ED visits (2020 – 2024) from the University Medical Center Schleswig-Holstein. CatBoost models were trained to (i) predict hospital admission (binary) and (ii) forecast the next specialty department (14-class). Results : Admission prediction achieved 77.3% accuracy after triage; department prediction reached 73.4%. Retrospective simulations on early 2025 data indicate that a conservative high-confidence operational policy of 70% could reduce mean boarding times by approximately 69 minutes and ED occupancy by 2.13 (8.15%)–5.37 (15.00%), depending on hour. Conclusions : Early prediction of admission and department enables proactive bed allocation and, in retrospective simulations, may reduce boarding times and mitigate ED crowding, supporting more efficient hospital resource management. Health sciences/Health care Physical sciences/Mathematics and computing Health sciences/Medical research emergency department crowding hospital admission prediction bed allocation CatBoost machine learning in healthcare Full Text Additional Declarations Competing interest reported. The authors Simon Schiff, Ralf Möller and Sebastian Wolfrum declare that they have no competing interests that could influence the objectivity or interpretation of this study. Author Mattis Hartwig is a co-founder and shareholder of singularIT GmbH. Author Natalie Kohler and Mareike Böckel are employees of singularIT GmbH. singularIT GmbH is a private company that is developing a software solution that uses the published prediction methods. A publication would not directly result in financial gain but indirectly add credibility to the developed software. The software accounts for less than 2% of the company's revenues strictly limiting the conflicting interest. 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. 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-8563126","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":575974784,"identity":"9abf32eb-f934-4111-b4de-c05a78350f78","order_by":0,"name":"Simon 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The authors Simon Schiff, Ralf Möller and Sebastian Wolfrum declare that they have no competing interests that could influence the objectivity or interpretation of this study.\nAuthor Mattis Hartwig is a co-founder and shareholder of singularIT GmbH.\nAuthor Natalie Kohler and Mareike Böckel are employees of singularIT GmbH.\nsingularIT GmbH is a private company that is developing a software solution that uses the published prediction methods.\nA publication would not directly result in financial gain but indirectly add credibility to the developed software.\nThe software accounts for less than 2% of the company's revenues strictly limiting the conflicting interest.","formattedTitle":"Early Prediction of Hospital Admission and Specialty for Proactive Bed Allocation in the Emergency Department","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"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":"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":"emergency department crowding, hospital admission prediction, bed allocation, CatBoost, machine learning in healthcare","lastPublishedDoi":"10.21203/rs.3.rs-8563126/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8563126/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e: Emergency department (ED) overcrowding is frequently driven by exit block, where patients with an admission decision wait for an inpatient bed. Early prediction of hospital admission and target specialty department may support proactive bed allocation.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e: We analyzed approximately 160,000 ED visits (2020 – 2024) from the University Medical Center Schleswig-Holstein. CatBoost models were trained to (i) predict hospital admission (binary) and (ii) forecast the next specialty department (14-class).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Admission prediction achieved 77.3% accuracy after triage; department prediction reached 73.4%. Retrospective simulations on early 2025 data indicate that a conservative high-confidence operational policy of 70% could reduce mean boarding times by approximately 69 minutes and ED occupancy by 2.13 (8.15%)–5.37 (15.00%), depending on hour.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions\u003c/strong\u003e: Early prediction of admission and department enables proactive bed allocation and, in retrospective simulations, may reduce boarding times and mitigate ED crowding, supporting more efficient hospital resource management.\u003c/p\u003e","manuscriptTitle":"Early Prediction of Hospital Admission and Specialty for Proactive Bed Allocation in the Emergency Department","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-19 06:21:56","doi":"10.21203/rs.3.rs-8563126/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"986a12a6-641d-430d-a1f5-37b91896b6b5","owner":[],"postedDate":"January 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61286515,"name":"Health sciences/Health care"},{"id":61286516,"name":"Physical sciences/Mathematics and computing"},{"id":61286517,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-01-29T01:09:33+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-19 06:21:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8563126","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8563126","identity":"rs-8563126","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}