[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120410-en":3,"doc-seo-120410-105":29,"detail-sidebar-cat-0-en-105":89},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":20,"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},120410,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Leveraging Artificial Intelligence for Smart Healthcare Management - Predicting and Reducing Patient Waiting Times with Machine Learning","A machine-learning-based methodology supports hospital resource management through predictive modeling and simulation enhancement to estimate patient waiting times. The proposed system integrates Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) to deliver accurate forecasts while accounting for personnel, hospital rooms, and special equipment. Validation uses a one-month hospital process simulation and regression metrics including MAE, RMSE, and R-squared. Ensemble approaches outperform traditional methods, achieving waiting-time prediction errors under ten minutes, while ANNs capture hidden patterns in patient flow and workload distribution. Results highlight AI decision support for reducing delays and balancing resources, enabling data-driven planning around demand variances and peak crowding periods.","Leveraging Artificial Intelligence for Smart Healthcare Management: Predicting and Reducing Patient Waiting Times with Machine Learning  \nKristijan CINCARa,b,* and Todor IVASCUa  \na West University of Timişoara, Vasile Pârvan Blvd., no. 4, 300223 Timişoara, Romania.  \nb Tibiscus University of Timişoara, Lascăr Catargiu Str, no. 6, 300223 Timişoara, Romania. E-mails: [kristijan.cincar@e-uvt.ro](kristijan.cincar@e-uvt.ro); [todor.ivascu@e-uvt.ro](todor.ivascu@e-uvt.ro)  \n* Author to whom correspondence should be addressed;  \nAbstract  \nThe paper focuses on a machine-learning-based methodology for predictive modelling and simulation enhancement of hospital resource management. The proposed system is built on a multitude of machinelearning algorithms such as Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs) to render accurate estimations of patient waiting times. Predictive modeling is then used to draw upon hospital resources that are important in consideration of the employed personnel, hospital rooms, and special equipment. To test its validity, a one-month simulation of the hospital process generated relevant statistics and other key resource utilization influencers. The performance of each model was assessed using key regression metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared (R²) . Preliminary experiments contrasted different machine-learning strategies, showing that the ensemble methods Random Forest and XGBoost far surpassed the traditional approaches with a mean absolute error for waiting time prediction of fewer than ten minutes. The use of deep learning models such as ANNs has also proven to yield favorable results in capturing hidden patterns in patient flow and distribution of hospital workload. The results indicate that decision support systems based on machine learning have the potential to have a tremendous impact on hospital efficiency, considering the decrease in patient waiting times and the more balanced allocation of resources. The system offered makes substantial contributions, as it provides actionable insights into demand variances and peak crowding periods that empower hospitals to make data-driven strategic decisions. Our study emphasizes the potential of artificial intelligence, simulation, and predictive analytics in health management and shows that a multi-model perspective can further enhance the allocation of resources and hospital management in practice.  \nKeywords: Machine Learning; Hospital Resource Management; Predictive Modeling; Decision Support Systems; Resource Allocation.","cbCaihb8U8i5N0gf","https://ap.wps.com/l/cbCaihb8U8i5N0gf","pdf",127781,1,"English","en",105,"# Abstract\n## Methodology and Models\n## Simulation Setup and Metrics\n## Results and Model Comparison\n## Decision Support and Practical Impact","[{\"question\":\"Which machine-learning models are used to predict patient waiting times?\",\"answer\":\"The study employs Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs).\"},{\"question\":\"How is the proposed system validated?\",\"answer\":\"Validation is performed using a one-month simulation of the hospital process, supported by regression metrics such as MAE, RMSE, and R-squared.\"},{\"question\":\"What results show about model performance and patient waiting-time reduction?\",\"answer\":\"Ensemble methods (Random Forest and XGBoost) outperform traditional approaches, producing mean absolute errors under ten minutes for waiting-time prediction and supporting more efficient resource allocation.\"}]","Leveraging Artificial Intelligence for Smart Healthcare Management - Predicting and Reducing Patient Waiting Times with Machine Learning | PDF",1785729906,3,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":84,"head_meta":86,"extra_data":88,"updated_unix":27},"leveraging-artificial-intelligence-for-smart-healthcare-management-predicting-and-reducing-patient-waiting-times-with-machine-learning","",{"@graph":35,"@context":83},[36,52,66],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,49],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":28},"https://docshare.wps.com/document/research-report/",{"item":50,"name":13,"@type":42,"position":51},"https://docshare.wps.com/document/leveraging-artificial-intelligence-for-smart-healthcare-management-predicting-and-reducing-patient-waiting-times-with-machine-learning/120410/",4,{"url":50,"name":13,"@type":53,"author":54,"headline":13,"publisher":56,"fileFormat":59,"inLanguage":22,"description":14,"dateModified":60,"datePublished":60,"encodingFormat":59,"isAccessibleForFree":61,"interactionStatistic":62},"DigitalDocument",{"name":9,"@type":55},"Person",{"url":40,"name":57,"@type":58},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":63,"interactionType":64,"userInteractionCount":4},"InteractionCounter",{"@type":65},"ViewAction",{"@type":67,"mainEntity":68},"FAQPage",[69,75,79],{"name":70,"@type":71,"acceptedAnswer":72},"Which machine-learning models are used to predict patient waiting times?","Question",{"text":73,"@type":74},"The study employs Random Forest Regression, XGBoost, Support Vector Regression (SVR), and Artificial Neural Networks (ANNs).","Answer",{"name":76,"@type":71,"acceptedAnswer":77},"How is the proposed system validated?",{"text":78,"@type":74},"Validation is performed using a one-month simulation of the hospital process, supported by regression metrics such as MAE, RMSE, and R-squared.",{"name":80,"@type":71,"acceptedAnswer":81},"What results show about model performance and patient waiting-time reduction?",{"text":82,"@type":74},"Ensemble methods (Random Forest and XGBoost) outperform traditional approaches, producing mean absolute errors under ten minutes for waiting-time prediction and supporting more efficient resource allocation.","https://schema.org",{"og:url":50,"og:type":85,"og:title":13,"og:site_name":57,"og:description":14},"article",{"robots":87,"canonical":50},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":90},[91,95,99,103,108,113,118,121,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":92,"show_sort_weight":93,"slug":94},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":96,"show_sort_weight":97,"slug":98},"Literature",80,"literature",{"id":51,"doc_module":4,"doc_module_name":45,"category_name":100,"show_sort_weight":101,"slug":102},"Exam",70,"exam",{"id":104,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},5,"Comic",60,"comic",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},6,"Technology",50,"technology",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},30,"research-report",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},9,"Religion & Spirituality",20,"religion-spirituality",{"id":124,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":124,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":104,"slug":136},19,"General","general"]