[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127077-en":3,"doc-seo-127077-105":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},127077,5909887256941,"Levi","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Applying the DMAIC Cycle and Machine Learning to Examine COVID-19’s Effects on Emergency Department - LOS","Emergency Department Length of Stay (ED-LOS) is analyzed over time using Lean Six Sigma (LSS), a healthcare quality approach originating from industrial practice. The DMAIC cycle provides structured methodological rigor by linking quantitative findings to process improvement. The study evaluates how COVID-19 affected ED-LOS at Penisola Hospital by applying LSS and forecasting ED_LOS with Random Forest, Decision Trees, and K-Nearest Neighbors models.","Applying the DMAIC Cycle and Machine Learning to Examine COVID-19’s Effects on Emergency Department-LOS  \nArianna, Scala  \nDepartment of Public Health, University of Naples “Federico II”, Naples, Italy  \nTeresa Angela, Trunfio*  \nDepartment of Advanced Biomedical Sciences, University of Naples“Federico II”, Naples, Italy  \nGiovanni, Improta  \nDepartment of Public Health, University of Naples “Federico II”, Naples, Italy; Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, Naples, Italy  \nAbstract  \nEmergency Department Length of Stay (LOS) over time, a methodology known as Lean Six Sigma (LSS) has garnered popularity in the healthcare industry, originating from industrial practices. Its tool, the DMAIC cycle, comprising five main components, offers methodological rigor by comparing quantitative results to aid in process improvement. This study examined the effect ofCOVID-19 on patient length of stay (ED-LOS) in the emergency department of Penisola Hospital, utilizing LSS, specifically focusing on the DMAIC cycle. Moreover, Machine learning models including Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN) were used to forecast the length of stay (ED_LOS) .  \nCCS Concepts  \n• General conference proceedings, Health informatics, Health care information systems;  \nKeywords  \nLean Six Sigma, DMAIC Cycle, Machine Learning, Length of stay, Emergency Department  \nACM Reference Format:  \nArianna, Scala, Teresa Angela, Trunfio*, and Giovanni, Improta. 2024. Applying the DMAIC Cycle and Machine Learning to Examine COVID-19’s Effects on Emergency Department-LOS. 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](https://doi.org/10.1145/3673971) . 3674008  \n1 INTRODUCTION  \nThe global COVID-19 pandemic has had a major impact on emergency departments (ED) . The main impacts of COVID-19 on disease include increased patient numbers, limited resources, staffing challenges, delays in treatment for non-COVID-19 illnesses, financial pressures, and the use of virtual care and telemedicine [1-3] .  \nThis work is licensed under a Creative Commons Attribution International 4 .0 License.  \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/10.1145/3673971.3674008](https://doi.org/10.1145/3673971.3674008)  \nEmergency departments must implement new policies and procedures to manage COVID-19 of patients to control, isolate suspected cases and prevent the spread of the virus in the hospital environment. Emergency department workers are suffering from increased workload, longer working hours and increased stress and burnout during the pandemic. Some individuals with illnesses unrelated to COVID-19 may not have sought treatment immediately due to fear [4] . Lussa’s Emergency Department implemented a telemedicine and virtual medical solution for triage, consultation and follow-up. lower medical costs. Managing the risk of infection with COVID-19 and patient burdens. In summary, the COVID-19 pandemic has dramatically changed radiation therapy operations, emphasizing the importance of health system preparedness, adaptability, and resilience [5, 6] . One way to examine how COVID-19 has affected ED operations is to conduct a process. analysis [7-9] . Lean Six Sigma (LSS) is one method that is gaining popularity in the healthcare industry. Lean Six Sigma is an approach that improves quality, reduces waste and increases organizational efficiency by combining the ideas of Six Sigma and Lean manufacturing. Organizations can reduce waste and errors while increasing productivity, quality and customer delight by combining Lean and Six Sigma approaches [10-13] . Typically, Lean Six Sigma initiatives follow the DMAIC framework [14-16] For e","cbCaiucpHrEULRsC","https://ap.wps.com/l/cbCaiucpHrEULRsC","pdf",221969,1,7,"English","en",105,"# Introduction\n# Background and Related Work\n# Methodology (LSS and DMAIC)\n# Data and Initial Observation\n## Table 1: Result of initial observation\n# Results and Analysis","[{\"question\":\"What methodology is used to examine changes in emergency department length of stay?\",\"answer\":\"The study applies Lean Six Sigma (LSS) using the DMAIC cycle to structure the analysis and support process improvement decisions based on quantitative results.\"},{\"question\":\"How does the study evaluate the impact of COVID-19 on ED length of stay?\",\"answer\":\"Data from 2019 (pre-pandemic) and 2020 (during the epidemic) are statistically compared to assess how COVID-19 influenced ED-LOS.\"},{\"question\":\"Which machine learning models are used to forecast ED_LOS?\",\"answer\":\"Random Forest (RF), Decision Trees (DT), and K-Nearest Neighbors (KNN) are used to forecast length of stay in the emergency department.\"}]","Applying the DMAIC Cycle and Machine Learning to Examine COVID-19’s Effects on Emergency Department - 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