[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127078-en":3,"doc-seo-127078-105":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":4,"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},127078,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","THE ANALYSIS OF COVID-19’S EFFECTS ON EMERGENCY DEPARTMENT-LOS USING LSS APPROACH AND MACHINE LEARNING","Using the Lean Six Sigma methodology with the DMAIC cycle, the study evaluates how COVID-19 affected patients’ duration of stay in the Emergency Department (ED-LOS) at Santa Maria della Pietà in Nola, Italy. ED-LOS rose markedly in 2020 compared with 2019. Across both datasets, machine learning models indicate age as the main predictor of ED-LOS variation, suggesting hospital management protocols as a key driver of the observed trend.","THE ANALYSIS OF COVID-19’S EFFECTS ON EMERGENCY DEPARTMENT-LOS USING LSS APPROACH AND MACHINE  \nLEARNING  \nTeresa ANGELA, TAT, Trunfio  \nDepartment of Advanced Biomedical Sciences, University of Naples“Federico II”, Naples, Italy  \nARIANNA, AS, SCALA∗  \nDepartment of Public Health, University of Naples “Federico II”, Naples, Italy  \nGIOVANNI, GI, Improta  \nDepartment of Public Health, University of Naples “Federico II”, Naples, ItalyInterdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, Naples, Italy  \nABSTRACT  \nUsing the Lean Six Sigma methodology, specifically the DMAIC cycle, the impact of COVID 19 on patients’ duration of stay in the Emergency Department (ED-LOS) of Santa Maria della Pietà, located in Nola, Italy, was investigated. Despite having originated in the manufacturing sector, LSS is now widely used in a variety of areas, such as healthcare, finance, and services. According to the findings, ED-LOS increased significantly in 2020 (the COVID19 year) as opposed to 2019 (the year before COVID19) . In both datasets, Machine Learning algorithms show that age is the main predictor. It is considered that the new protocols implemented by the hospital management are the main cause of the trend.  \nCCS CONCEPTS  \n• General conference proceedings, Health informatics, Health care information systems;  \nKEYWORDS  \nLean six Sigma, Machine Learning, DMAIC, LOS, Emergency department  \nACM Reference Format:  \nTeresa ANGELA, TAT, Trunfio, ARIANNA, AS, SCALA, and GIOVANNI, GI, Improta. 2024. THE ANALYSIS OF COVID-19’S EFFECTS ON EMERGENCY DEPARTMENT-LOS USING LSS APPROACH AND MACHINE LEARNING. In 2024 8th International Conference on Medical and Health Informatics (ICMHI 2024), May 17–19, 2024, Yokohama, Japan. ACM, New York, NY, USA, 7 pages. [https://doi.org/10.1145/3673971.3674007](https://doi.org/10.1145/3673971.3674007)  \n1 INTRODUCTION  \nLean Six Sigma is a methodology that combines the concepts of Lean Manufacturing and Six Sigma to improve the efficiency of  \n∗Corresponding author: [ariannascala7@gmail.com](ariannascala7@gmail.com).  \nPermission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the owner/author(s) .  \nICMHI 2024, May 17–19, 2024, Yokohama, Japan © 2024 Copyright held by the owner/author(s) .  \nACM ISBN 979-8-4007-1687-4/24/05  \n[https://doi.org/10.1145/3673971.3674007](https://doi.org/10.1145/3673971.3674007)  \norganizational processes. Despite having its roots in the manufacturing industry, it has now expanded to numerous other industries, including the healthcare industry.  \nThe Lean methodology places a strong emphasis on identifying and eliminating waste from processes in order to boost output and save costs. On the other hand, the data-driven Six Sigma technique seeks to minimize operational variability and errors in order to attain near-perfect quality. It aims to lessen process variance and defects by employing statistical techniques and tools, mainly DMAIC (Define, Measure, Analyze, Improve, Control) or DMADV (Define, Measure, Analyze, Design, Verify) processes [1-6] .  \nLean Six Sigma projects improve processes and yield measurable results by applying the DMAIC problem-solving methodology. DMAIC stands for Define, Measure, Analyze, Improve, and Control. Each stage represents a milestone in the process of identifying and solving problems [7-9] .  \nDefining the issue or area in need of improvement, outlining the project’s objectives, and determining its scope are all done at this phase of the project. One of the most important products of this phase is a project charter that describes the goals, timetable, and s","cbCaihI4vhy5TExd","https://ap.wps.com/l/cbCaihI4vhy5TExd","pdf",219136,1,7,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"Which methodology and cycle are used to study ED-LOS during COVID-19?\",\"answer\":\"The study applies Lean Six Sigma using the DMAIC cycle (Define, Measure, Analyze, Improve, Control) to investigate ED-LOS changes.\"},{\"question\":\"How did emergency department length of stay change from 2019 to 2020?\",\"answer\":\"ED-LOS increased significantly in 2020 compared with 2019, the year immediately preceding COVID-19.\"},{\"question\":\"What does the machine learning analysis identify as the main predictor of ED-LOS?\",\"answer\":\"Machine learning results show that age is the main predictor across both datasets.\"}]","THE ANALYSIS OF COVID-19’S EFFECTS ON EMERGENCY DEPARTMENT-LOS USING LSS APPROACH AND MACHINE LEARNING | PDF",1785936720,18,{"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},"the-analysis-of-covid-19s-effects-on-emergency-department-los-using-lss-approach-and-machine-learning","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/the-analysis-of-covid-19s-effects-on-emergency-department-los-using-lss-approach-and-machine-learning/127078/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which methodology and cycle are used to study ED-LOS during COVID-19?","Question",{"text":75,"@type":76},"The study applies Lean Six Sigma using the DMAIC cycle (Define, Measure, Analyze, Improve, Control) to investigate ED-LOS changes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How did emergency department length of stay change from 2019 to 2020?",{"text":80,"@type":76},"ED-LOS increased significantly in 2020 compared with 2019, the year immediately preceding COVID-19.",{"name":82,"@type":73,"acceptedAnswer":83},"What does the machine learning analysis identify as the main predictor of ED-LOS?",{"text":84,"@type":76},"Machine learning results show that age is the main predictor across both datasets.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]