Lowering Barriers to AI Adoption in Regional Hospitals: Predicting Patient Volumes from Minimal Data

Lowering Barriers to AI Adoption in Regional Hospitals: Predicting Patient Volumes from Minimal Data | 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 Lowering Barriers to AI Adoption in Regional Hospitals: Predicting Patient Volumes from Minimal Data Stefan Förstel, Markus Förstel, Markus Gallistl, Dario Zanca, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7530462/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 Background: Artificial intelligence (AI) is increasingly promoted as a tool to enhance hospital efficiency and patient care. Yet, the adoption of AI in regional hospitals remains limited, often due to data scarcity, high implementation costs, and concerns regarding compliance with data protection laws. This study investigates how predictive analytics based on minimal datasets—limited to admission and discharge timestamps, enriched with contextual public data—can already provide actionable insights for hospital operations. Methods: Using routine hospital data, we aggregated daily admissions, discharges, and inpatient loads, and combined them with external features such as weather and public holidays. While 30 years of data were available, we demonstrate that a training window of only the most recent five years is sufficient to achieve high predictive accuracy. Random Forest models were applied to forecast patient numbers, with performance assessed via mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The models were designed with a focus on pragmatic AI adoption: simple to implement, explainable, and fully compliant with GDPR through inherent anonymization. Results: Our models achieved high predictive accuracy, capturing both weekly cycles and seasonal fluctuations. Daily inpatient forecasts reached a MAPE of 2.4%, corresponding to an average error of only 10 patients. These results demonstrate that even low-complexity AI can provide reliable decision support for staffing and resource allocation, reducing the risk of overcrowding and improving care delivery. Conclusions: Our findings show that low-complexity, data-efficient AI can provide robust forecasts with minimal inputs, lowering barriers to adoption in regional hospitals while maintaining strong compliance with data protection frameworks.This enables immediate improvements in operational planning, reduces overcrowding risks, and supports care delivery under increasing system pressures. Artificial Intelligence in Patient Care Nursing Management Staffing Strategies Hospital Capacity Planning Patient Flow Forecasting Minimal Dataset Decision Support Systems Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 16 Jan, 2026 Reviews received at journal 05 Jan, 2026 Reviews received at journal 03 Jan, 2026 Reviews received at journal 16 Dec, 2025 Reviewers agreed at journal 22 Nov, 2025 Reviewers agreed at journal 21 Nov, 2025 Reviewers agreed at journal 20 Nov, 2025 Reviewers agreed at journal 19 Nov, 2025 Reviewers invited by journal 27 Oct, 2025 Editor assigned by journal 05 Sep, 2025 Submission checks completed at journal 05 Sep, 2025 First submitted to journal 03 Sep, 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-7530462","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":512441346,"identity":"37e14434-0b32-4521-b9f3-d2ba5c9d7cfb","order_by":0,"name":"Stefan Förstel","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABCUlEQVRIie2RMUsDMRTHXwi8LrFd7zi8+woJBxWp4le5I2BH125NOHiT4uog+DUcWwKddHesdFWwOBVPMGddHNLi5pDf8MiD/Pi/lwBEIv+Ut+/KZ9uu37PLrg3fR2A320PVVQkonPyrkujdSnHXzM3mHgpAPl9N2jbHdMXeBYzykCIXWNurB1AGUatHkiVmmmcCxmVQQXG0PCBgprgcptbImrKLmVdcbUKD0WBtPwnOjHc3ppVTSh3/8Mo0pMBCsMan1F4ZMoOywoRjl1KFdzlXzSElmvwuqaVSkdB4fCvHKjhY457tK52cXvsXW5s2LwY9x59eJqMilPJD4v/nd/oeIRKJRCI7+QJV+kkniV7XpwAAAABJRU5ErkJggg==","orcid":"","institution":"Technical University Amberg-Weiden","correspondingAuthor":true,"prefix":"","firstName":"Stefan","middleName":"","lastName":"Förstel","suffix":""},{"id":512441347,"identity":"f96d2011-01ad-4b88-a31a-2ce40f90da87","order_by":1,"name":"Markus Förstel","email":"","orcid":"","institution":"Technical University Amberg-Weiden","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Förstel","suffix":""},{"id":512441348,"identity":"cdfb94c0-794d-4365-b8f0-8a57f084ceb6","order_by":2,"name":"Markus Gallistl","email":"","orcid":"","institution":"Kliniken Nordoberpfalz AG","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Gallistl","suffix":""},{"id":512441349,"identity":"a4830894-4935-4dd5-940c-54a42e667c39","order_by":3,"name":"Dario Zanca","email":"","orcid":"","institution":"Technische Fakultät, Friedrich-Alexander Universität Erlangen-Nürnberg","correspondingAuthor":false,"prefix":"","firstName":"Dario","middleName":"","lastName":"Zanca","suffix":""},{"id":512441350,"identity":"ebd39fd4-8040-4775-ab96-5d1bf6c29b72","order_by":4,"name":"Bjoern M. 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Data","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":"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":"Artificial Intelligence in Patient Care, Nursing Management, Staffing Strategies, Hospital Capacity Planning, Patient Flow Forecasting, Minimal Dataset, Decision Support Systems","lastPublishedDoi":"10.21203/rs.3.rs-7530462/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7530462/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground:\u003c/h2\u003e\u003cp\u003eArtificial intelligence (AI) is increasingly promoted as a tool to enhance hospital efficiency and patient care. Yet, the adoption of AI in regional hospitals remains limited, often due to data scarcity, high implementation costs, and concerns regarding compliance with data protection laws. This study investigates how predictive analytics based on minimal datasets\u0026mdash;limited to admission and discharge timestamps, enriched with contextual public data\u0026mdash;can already provide actionable insights for hospital operations.\u003c/p\u003e\u003ch2\u003eMethods:\u003c/h2\u003e\u003cp\u003eUsing routine hospital data, we aggregated daily admissions, discharges, and inpatient loads, and combined them with external features such as weather and public holidays. While 30 years of data were available, we demonstrate that a training window of only the most recent five years is sufficient to achieve high predictive accuracy. Random Forest models were applied to forecast patient numbers, with performance assessed via mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean squared error (RMSE). The models were designed with a focus on pragmatic AI adoption: simple to implement, explainable, and fully compliant with GDPR through inherent anonymization.\u003c/p\u003e\u003ch2\u003eResults:\u003c/h2\u003e\u003cp\u003eOur models achieved high predictive accuracy, capturing both weekly cycles and seasonal fluctuations. Daily inpatient forecasts reached a MAPE of 2.4%, corresponding to an average error of only 10 patients. These results demonstrate that even low-complexity AI can provide reliable decision support for staffing and resource allocation, reducing the risk of overcrowding and improving care delivery.\u003c/p\u003e\u003ch2\u003eConclusions:\u003c/h2\u003e\u003cp\u003eOur findings show that low-complexity, data-efficient AI can provide robust forecasts with minimal inputs, lowering barriers to adoption in regional hospitals while maintaining strong compliance with data protection frameworks.This enables immediate improvements in operational planning, reduces overcrowding risks, and supports care delivery under increasing system pressures.\u003c/p\u003e","manuscriptTitle":"Lowering Barriers to AI Adoption in Regional Hospitals: Predicting Patient Volumes from Minimal Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-14 00:39:37","doi":"10.21203/rs.3.rs-7530462/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-01-16T12:23:10+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-05T15:07:52+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-01-04T04:40:09+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-16T15:12:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"127554692190345205154471241862793960532","date":"2025-11-22T21:15:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"274001834599956889827702541250019765304","date":"2025-11-21T17:46:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"146974218201610317267066760300859828588","date":"2025-11-20T07:16:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195123016518884897168128152460393387876","date":"2025-11-19T18:17:46+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-10-27T15:23:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-05T07:59:02+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-09-05T07:58:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-09-03T22:09:02+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":"bef39c7b-c3fb-42ef-806a-103625cb99c7","owner":[],"postedDate":"October 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-17T13:25:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-14 00:39:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7530462","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7530462","identity":"rs-7530462","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}