An Optimization Protocol for MRI Examination Resource Allocation Based on Demand Forecasting and Linear Programming

An Optimization Protocol for MRI Examination Resource Allocation Based on Demand Forecasting and Linear Programming | 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 An Optimization Protocol for MRI Examination Resource Allocation Based on Demand Forecasting and Linear Programming Zhongbin Zhou, Hanyu Zhou, Yuanyuan Qiao, Zhihan Gao, Ying Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5598439/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Objective: The accessibility of medical services in Mainland China had been on the rise, leading to a surge in the number of Magnetic Resonance Imaging (MRI) scans. This increase had caused substantial delays in MRI examination queues at large hospitals. With MRI equipment and exams being costly, over-purchasing machines could lead to underutilization of resources. It was, therefore, crucial to devise a comprehensive method that could shorten patient wait times and optimize the use of medical resources within hospitals. Method: The research had utilized daily MRI examination application data from a hospital covering the period from July 1, 2017, to November 30, 2022. The Autoregressive Integrated Moving Average (ARIMA) model and the AutoRegressive Integrated Moving Average with exogenous (ARIMAX) model were developed using SAS (version 9.3) software. Moreover, Non-AutoRegressive (NAR) and Non-AutoRegressive with exogenous (NARX) models were built using MATLAB (version R2015b) to forecast future MRI examination demands. The predictive accuracy of these models was then assessed and compared. Based on the prediction outcomes, an Integer Linear Programming model was employed to calculate the optimal number of MRI examinations per machine per day, targeting cost reduction. An optimization flowchart for MRI examination resource allocation was developed by integrating critical process components, thus streamlining and systematizing the optimization process to improve efficiency. Results: Analysis of the data revealed a weekly cyclical trend in MRI examination applications. Among the ARIMA, ARIMAX, NAR, and NARX models evaluated for their predictive skills, the NARX model emerged as the most accurate for forecasting. An Integer Linear Programming (ILP) model was utilized to plan the number of examinations for each MRI machine, effectively reducing costs. An optimization flowchart was developed to integrate key factors in MRI examination resource allocation, streamlining and systematizing the optimization process to enhance work efficiency. Conclusion: This study offers a comprehensive protocol for optimizing MRI examination resource allocation, combining the predictive power of the NARX model, the planning capabilities of the Integer Linear Programming model, and the integration of other relevant factors via an optimization flowchart. Clinical trial number: not applicable. Health sciences/Health care Health sciences/Medical research Full Text Additional Declarations No competing interests reported. Supplementary Files tables.docx Cite Share Download PDF Status: Published Journal Publication published 29 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 29 Jan, 2025 Reviews received at journal 24 Jan, 2025 Reviews received at journal 17 Jan, 2025 Reviewers agreed at journal 17 Jan, 2025 Reviewers agreed at journal 16 Jan, 2025 Reviewers invited by journal 23 Dec, 2024 Editor assigned by journal 20 Dec, 2024 Editor invited by journal 20 Dec, 2024 Submission checks completed at journal 19 Dec, 2024 First submitted to journal 07 Dec, 2024 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-5598439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":392954409,"identity":"c234e271-59df-42cf-8fad-2969ebf852d6","order_by":0,"name":"Zhongbin Zhou","email":"","orcid":"","institution":"The 6 th Medical Center of PLA General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Zhongbin","middleName":"","lastName":"Zhou","suffix":""},{"id":392954410,"identity":"1bfa7feb-39c3-4940-b653-93fa5fde28d7","order_by":1,"name":"Hanyu Zhou","email":"","orcid":"","institution":"China Unicom Beijing 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This increase had caused substantial delays in MRI examination queues at large hospitals. With MRI equipment and exams being costly, over-purchasing machines could lead to underutilization of resources. It was, therefore, crucial to devise a comprehensive method that could shorten patient wait times and optimize the use of medical resources within hospitals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod:\u003c/strong\u003e The research had utilized daily MRI examination application data from a hospital covering the period from July 1, 2017, to November 30, 2022. The Autoregressive Integrated Moving Average (ARIMA) model and the AutoRegressive Integrated Moving Average with exogenous (ARIMAX) model were developed using SAS (version 9.3) software. Moreover, Non-AutoRegressive (NAR) and Non-AutoRegressive with exogenous (NARX) models were built using MATLAB (version R2015b) to forecast future MRI examination demands. 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