Intelligent ERP Framework for Financial Optimization in Smart Healthcare Institutions

Intelligent ERP Framework for Financial Optimization in Smart Healthcare Institutions | 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 Intelligent ERP Framework for Financial Optimization in Smart Healthcare Institutions Sukumar Reddy Beereddy Beereddy, Veena Thirumala Reddy Reddy, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9298309/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Smart healthcare institutions are increasingly dependent on IoT-enabled diagnostics, AI-enabled clinical systems and cloud-based hospital infrastructures, however, monetary management systems in Enterprise Resource Planning (ERP) systems are still mostly transactional and reactive. Traditional ERP platforms are void of adaptive financial intelligence to deal with dynamic patient inflow, reimbursement variance and varying operational costs. Based on this work, an Intelligent ERP Framework combining predictive and constrained financial optimization algorithms is proposed to improve cost minimization, revenue forecasting accuracy and long-term financial sustainability in smart healthcare environments. A hybrid architecture that combines machine learning-based revenue prediction, multi-variable cost modelling and real-time ERP financial data streams was built and tested using synthetic 24-month-long hospital financial datasets that contained 50,000 simulated patient records. Simulation results show an increase in Revenue Growth Rate by 17.9% and in Cost Reduction Index by 15.4% and increase in the Financial Efficiency Ratio by 1.36 from 1.10 with respect to conventional ERP systems. Operational cost variability decreased by 22% and billing cycle delay decreased by 18% and the accuracy of revenue forecast rose to 94.6%. The proposed framework resulted in a Composite Optimization Score gain from 0.62 to 0.89 implying significant financial stability gains. As part of the ERP architectures with predictive intelligence, the Intelligent ERP Framework facilitates adaptive financial decision making and budget allocation strategies and helps maintain a better revenue sustainability in a digitally transformed smart healthcare institution. Smart Healthcare Intelligent ERP Financial Optimization Predictive Analytics Cost Modelling Revenue Forecasting Healthcare Informatics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 28 Apr, 2026 Reviewers agreed at journal 26 Apr, 2026 Reviews received at journal 22 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers invited by journal 19 Apr, 2026 Editor assigned by journal 13 Apr, 2026 Submission checks completed at journal 11 Apr, 2026 First submitted to journal 11 Apr, 2026 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. 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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-9298309","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":629738629,"identity":"c6f37eea-66c0-4c13-8c7c-f511177fed6b","order_by":0,"name":"Sukumar Reddy Beereddy 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