[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120358-en":3,"doc-seo-120358-105":30,"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":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120358,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",7,"Healthcare","Optimizing Healthcare Management Systems with AI and Machine Learning - Volume XXVI - 2025","This paper explores how artificial intelligence and machine learning can optimize healthcare management systems amid rising complexity. It focuses on decision support, resource allocation, and patient-care improvements through four core algorithms, including Logistic Regression, Random Forest, Support Vector Machine, and Neural Predictive Networks. Reported outcomes include 91.5% accuracy for the neural network model, alongside improved efficiency and reduced patient wait time (up to 20%) in an edge-computing setting. It also addresses cost reduction, financial-risk prediction, and the need for future transparency and ethical data management to mitigate privacy and bias concerns.","Optimizing Healthcare Management Systems with AI and Machine Learning SEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nOptimizing Healthcare Management Systems with AI and Machine Learning  \nDr Rajesh Gupta1, Nutan Gusain2, Dr. Bajirao Subhash Shirole3, Dr. Mahendra T. Jagtap4,  \nSanjose A Thomas5, Dr. SANTOSH KUMAR6  \n1 Designation Associate Professor  \nLovely Professional University, district Phagwara city jalandhar, state Punjab [email ](email rajeshgpt47671@gmail.com)[rajeshgpt47671@gmail.com](email rajeshgpt47671@gmail.com)  \n2 Designation: Assistant Professor  \nDepartment: Department of CSE,School of Computing Science and Engineering  \nInstitute: Galgotias University, District: Gautam Buddha Nagar  \nCity: Greater Noida, State:UP  \nEmail [id](id-nutan.gusain41@gmail.com)[-nutan.gusain41@gmail.com](id-nutan.gusain41@gmail.com)  \n3 Designation: Assistant Professor, Department: Computer Engineering  \nInstitute: Sanghavi College of Engineering, Nashik  \nDistrict: Nashik, City: NashikState: Maharashtra  \nMail id: [baji.shirole@gmail.com](baji.shirole@gmail.com)  \n4 Designation:-Associate professor, Department: Computer Engineering  \nInstitute: S.M.E.S, Sanghavi College of Engineering, Nashik  \nDistrict: Nashik, City: Nashik  \nState: Maharashtra  \nEmail [id-](id-mtjagtap05@gmail.com)[mtjagtap05@gmail.com](id-mtjagtap05@gmail.com)  \n5 Research Scholar  \nDepartment of Sociology & Centre for Research  \nSt. Teresa's College (Autonomous), Ernakulam-682011  \n[sanjosethomas.thomas065@gmail.com](sanjosethomas.thomas065@gmail.com)  \n6 Designation: PROFESSOR  \nDepartment: Department of Computer Science  \nInstitute: ERA University, Lucknow, U.P.  \nDistrict: Lucknow, City: Lucknow  \nState: Uttar Pradesh  \nEmail id – [dr.santoshkumarresearch@gmail.com](dr.santoshkumarresearch@gmail.com)  \nOptimizing Healthcare Management Systems with AI and Machine Learning SEEJPH Volume XXVI, S1, 2025, ISSN: 2197-5248; Posted:05-01-2025  \nKEYWORDS  \nHealthcare Management, Artificial  \nABSTRACT  \nThis paper explores the optimization of healthcare management systems using Artificial Intelligence (AI) and Machine Learning (ML) . As the complexity of healthcare systems continues to grow, AI and ML have emerged as key tools  \nto improve decision-making, resource allocation, and patient care. This paper Intelligence,  \nprovides a detailed discussion on four AI algorithms, namely Logistic Machine Learning, Regression, Random Forest, Support Vector Machine (SVM), and Neural  \nPredictive Networks, and their application in the prediction of patient outcomes, including  \nAnalytics, Postoperative Length of Stay.  \npostoperative LOS and disease diagnosis. Experimental results indicate that the accuracy of the Neural Network model was 91.5%, outperforming other algorithms. The precision of the Random Forest model was 87.3%, while therecall ofSVM was 82.4% . Apart from the above point, the current research has noted AI application use for the reduction of healthcare-related cost optimization via predicting financial risk and improving a management strategy pertaining to patient data. Machine learning implementation in an edge computing facility showcased a drop in patient wait time by up to 20% and achieved 15% increase in overall efficiency. Promising results and huge challenges exist side by side with model interpretation and data protection issues. This study highlights the requirement for future AI transparency and ethical data management in order to achieve the full potential of AI in healthcare.  \nI. INTRODUCTION  \nHealthcare management systems are being transformed by the inclusion of artificial intelligence and machine learning. For years, traditional healthcare management has faced inefficiencies in administrative tasks, high operational costs, resource allocation issues, and delays in the diagnosis and treatment of patients. In healthcare, AI and ML offer new technological ways to streamline the processes, better influence decisions by the sys","cbCaiboaWFXsEPuZ","https://ap.wps.com/l/cbCaiboaWFXsEPuZ","pdf",470094,1,14,"English","en",105,"# Introduction\n## Related Works\n## Algorithm Applications\n## Experimental Results\n## Challenges and Future Directions","[{\"question\":\"How do AI and machine learning improve healthcare management systems?\",\"answer\":\"They streamline administrative processes, support real-time analysis of large datasets, enhance decision-making, and improve patient outcomes through predictive modeling and decision support.\"},{\"question\":\"Which AI/ML algorithms are discussed for patient outcome prediction?\",\"answer\":\"The paper discusses Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Predictive Networks for predicting patient outcomes such as postoperative length of stay and disease diagnosis.\"},{\"question\":\"What performance and efficiency results are reported?\",\"answer\":\"The neural network model reaches 91.5% accuracy, Random Forest shows 87.3% precision, and SVM recall is 82.4%. Edge computing reduces patient wait time by up to 20% and increases overall efficiency by 15%.\"}]","Optimizing Healthcare Management Systems with AI and Machine Learning - Volume XXVI - 2025 | PDF",1785729648,35,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"optimizing-healthcare-management-systems-with-ai-and-machine-learning-volume-xxvi-2025","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/optimizing-healthcare-management-systems-with-ai-and-machine-learning-volume-xxvi-2025/120358/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How do AI and machine learning improve healthcare management systems?","Question",{"text":75,"@type":76},"They streamline administrative processes, support real-time analysis of large datasets, enhance decision-making, and improve patient outcomes through predictive modeling and decision support.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which AI/ML algorithms are discussed for patient outcome prediction?",{"text":80,"@type":76},"The paper discusses Logistic Regression, Random Forest, Support Vector Machine (SVM), and Neural Predictive Networks for predicting patient outcomes such as postoperative length of stay and disease diagnosis.",{"name":82,"@type":73,"acceptedAnswer":83},"What performance and efficiency results are reported?",{"text":84,"@type":76},"The neural network model reaches 91.5% accuracy, Random Forest shows 87.3% precision, and SVM recall is 82.4%. 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