Assessment of patient waiting time and queue length using simulation model and machine learning techniques

Assessment of patient waiting time and queue length using simulation model and machine learning techniques | 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 Assessment of patient waiting time and queue length using simulation model and machine learning techniques Jagriti Gupta, Naresh Sharma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6159876/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 An accurate estimation of patient waiting time and queue length are essential for defining patient satisfaction, optimum resource allocation, healthcare response and acceptability of healthcare facilities. The conventional statistical prediction approach lacks precision, leading to patient and personnel difficulties. Methods To address this, a simulation model that optimises the different queuing configurations and machine learning approaches that facilitate the dynamic prediction of waiting times based on many criteria, including appointment scheduling, patient demographics, and facility workload, has been applied in this study to predict the wait time of patients and queue length before receiving the healthcare service. The TORA model has been applied to study the probabilistic nature of queue formation and associated waiting time. Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR) were applied to model the correlation between significant factors and waiting or delay periods. The RF was applied to capture the intricate, nonlinear interactions, the SVR to record the high-dimensional environments, and LR to identify the most appropriate method for this application. Results The results suggested that machine learning techniques outperformed the simulation model. Among machine learning techniques, RF performed over SVR and LR to manage the intricacies of real-world data, although SVR offered significant insights owing to its interpretability. Conclusion Applying machine learning techniques to predict waiting time could improve patient satisfaction and experience and assist in optimising healthcare service provision. This study emphasises the significance of data-driven decision-making in minimising delays and enhancing patient flow in OPDs. Waiting time Queue length machine learning random forest support vector regression Full Text Additional Declarations No competing interests reported. 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-6159876","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":435002598,"identity":"1c6fd339-86bd-4f3d-87d3-4d449d6de383","order_by":0,"name":"Jagriti Gupta","email":"","orcid":"","institution":"GD Goenka University","correspondingAuthor":false,"prefix":"","firstName":"Jagriti","middleName":"","lastName":"Gupta","suffix":""},{"id":435002599,"identity":"5f02a9a6-ab95-4fc3-ae91-f2dc4f120203","order_by":1,"name":"Naresh Sharma","email":"data:image/png;base64,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","orcid":"","institution":"GD Goenka University","correspondingAuthor":true,"prefix":"","firstName":"Naresh","middleName":"","lastName":"Sharma","suffix":""}],"badges":[],"createdAt":"2025-03-05 07:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6159876/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6159876/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79420153,"identity":"fd9136d3-fb10-4d7b-b1d8-833d44ae5f01","added_by":"auto","created_at":"2025-03-28 08:09:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":522903,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptEVIN2802.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6159876/v1_covered_27938260-9bb1-4dbe-9060-bf6ba2916ffd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessment of patient waiting time and queue length using simulation model and machine learning techniques","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Waiting time, Queue length, machine learning, random forest, support vector regression","lastPublishedDoi":"10.21203/rs.3.rs-6159876/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6159876/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eAn accurate estimation of patient waiting time and queue length are essential for defining patient satisfaction, optimum resource allocation, healthcare response and acceptability of healthcare facilities. The conventional statistical prediction approach lacks precision, leading to patient and personnel difficulties.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo address this, a simulation model that optimises the different queuing configurations and machine learning approaches that facilitate the dynamic prediction of waiting times based on many criteria, including appointment scheduling, patient demographics, and facility workload, has been applied in this study to predict the wait time of patients and queue length before receiving the healthcare service. The TORA model has been applied to study the probabilistic nature of queue formation and associated waiting time. Linear Regression (LR), Random Forest (RF), and Support Vector Regression (SVR) were applied to model the correlation between significant factors and waiting or delay periods. The RF was applied to capture the intricate, nonlinear interactions, the SVR to record the high-dimensional environments, and LR to identify the most appropriate method for this application.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe results suggested that machine learning techniques outperformed the simulation model. Among machine learning techniques, RF performed over SVR and LR to manage the intricacies of real-world data, although SVR offered significant insights owing to its interpretability.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eApplying machine learning techniques to predict waiting time could improve patient satisfaction and experience and assist in optimising healthcare service provision. 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