[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127087-en":3,"doc-seo-127087-105":29,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},127087,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Big Data and Machine Learning for Healthcare Resource Allocation and Optimization - Resource Allocation, Predictive Analytics, Optimization","Healthcare systems face increasing pressure to allocate limited resources effectively due to growing populations, rising healthcare costs, and the escalating complexity of medical needs. Big data and machine learning (ML) technologies provide transformative capabilities for data-driven decision-making, operational efficiency, and improved patient outcomes. The paper examines integration approaches for predictive analytics in patient admissions, staffing optimization, inventory management, and strategic planning, while analyzing privacy, interoperability, and algorithmic bias and proposing directions for more efficient and equitable healthcare systems.","BIG DATA AND MACHINE LEARNING FOR HEALTHCARE RESOURCE ALLOCATION AND OPTIMIZATION  \nSEEJPH Volume XXV,S2,2024, ISSN: 2197-5248;Posted:05-12-2024  \nBIG DATA AND MACHINE LEARNING FOR HEALTHCARE RESOURCE ALLOCATION AND  \nOPTIMIZATION  \nKapil Arora1, Prema P2, Hemalatha Yadav J3, K. Kavitha4, Biswo Ranjan Mishra5,  \nK. Suresh Kumar6  \n1Professor-Finance, Alliance School of Business, Alliance University, Bangalore.  \n2Assistant Professor, Department of Information Technology, Panimalar Engineering College. 3Doctoral Scholar, Alliance School of Business, Alliance University.  \n4Assistant Professor, Department of Mathematics, Velammal Institute of technology, Panchetti. 5Assistant Professor, Utkal University (DDCE)  \n6Professor, MBA Department, Panimalar Engineering College, Varadarajapuram, Poonamallee, Chennai-600123. (Orcid: 0000-0002-3912-3687)  \n[Email:](Email: profkapilarora@gmail.com)[ profkapilarora@gmail.com](Email: profkapilarora@gmail.com), [hemalathayadav10@gmail.com](hemalathayadav10@gmail.com),  \n[premapersonal@gmail.com](premapersonal@gmail.com), [yhemalathaphd20@bus.alliance.edu.in](yhemalathaphd20@bus.alliance.edu.in), [kavibiet@gmail.com](kavibiet@gmail.com),  \n[biswomishra@gmail.com](biswomishra@gmail.com), [pecmba19@gmail.com](pecmba19@gmail.com)  \nKEYWORDS  \nBig Data, Machine Learning, Healthcare  \nABSTRACT  \nHealthcare systems face increasing pressure to allocate limited resources effectively due to growing populations, rising healthcare costs, and the increasing complexity of medical needs. The advent of big data and machine  \nResource Allocation, learning (ML) technologies offers transformative potential for addressing Optimization, these challenges. This paper explores the integration of big data and ML in Predictive Analytics, healthcare resource allocation and optimization, focusing on how these  \nData-Driven technologies enable data-driven decision-making, improve operational  \nDecision-Making, efficiency, and enhance patient outcomes. We discuss applications such as Healthcare Systems, predictive modeling for patient admissions, optimization of staffing, Operational inventory management, and strategic planning. Additionally, challenges Efficiency, Patient such as data privacy, interoperability, and algorithmic bias are analyzed, and Outcomes, potential solutions are proposed. This paper concludes with insights into Algorithmic Bias future directions for research and practice in leveraging big data and ML to create more efficient and equitable healthcare systems.  \nINTRODUCTION  \nThe rapid proliferation of data-driven technologies has transformed industries worldwide, with healthcare emerging as one of the most significantly impacted sectors. The integration of big data and machine learning (ML) into healthcare resource allocation and optimization has opened new avenues for improving efficiency, reducing costs, and enhancing patient outcomes. From hospital bed management and workforce allocation to optimizing the distribution of medical supplies, these technologies are proving essential in addressing the multifaceted challenges of modern healthcare systems.  \nThe healthcare industry faces a persistent challenge of managing limited resources in the face of increasing demand. Aging populations, the rise in chronic diseases, and unexpected crises like the COVID-19 pandemic have highlighted the importance of efficient resource allocation. Traditional methods often fall short due to their reliance on static models and limited datasets, leading to  \n3998 | P a g e  \nBIG DATA AND MACHINE LEARNING FOR HEALTHCARE RESOURCE ALLOCATION AND OPTIMIZATION  \nSEEJPH Volume XXV,S2,2024, ISSN: 2197-5248;Posted:05-12-2024  \ninefficiencies and disparities in healthcare delivery. Big data and ML provide dynamic, scalable, and predictive solutions that promise to revolutionize healthcare resource management.  \nBig data in healthcare refers to the massive volumes of structured and unstructured data generated from various sources,","cbCaibDORtzffpfX","https://ap.wps.com/l/cbCaibDORtzffpfX","pdf",375893,1,"English","en",105,"# Introduction\n## Healthcare resource allocation challenges\n## Big data in healthcare data characteristics\n## Machine learning methods in healthcare\n## Literature review and research gaps\n## Evolution of approaches (2010–2015, 2016 onwards)","[{\"question\":\"How do big data and ML improve healthcare resource allocation and optimization?\",\"answer\":\"They enable predictive, data-driven decisions that improve operational efficiency, reduce costs, and enhance patient outcomes by forecasting resource needs and optimizing supply chains.\"},{\"question\":\"What healthcare resource areas does the paper focus on for applying these technologies?\",\"answer\":\"It highlights predictive modeling for patient admissions, staffing optimization, operational inventory management, and strategic planning.\"},{\"question\":\"What key challenges are discussed when using big data and ML in healthcare?\",\"answer\":\"The paper analyzes data privacy, interoperability, and algorithmic bias, and discusses potential solutions and future directions.\"}]","Big Data and Machine Learning for Healthcare Resource Allocation and Optimization - 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