[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124970-en":3,"doc-seo-124970-105":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},124970,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","Machine Learning Overbooking Framework for Outpatient Appointments - Improving Resource Allocation and Correcting Socioeconomic Bias","Outpatient care relies heavily on effective appointment scheduling, yet patient no-shows undermine service quality by causing treatment delays and financial losses. As artificial intelligence advances, combining overbooking with machine learning enables individualized scheduling, but raises concerns about group bias and unfair service outcomes. This study identifies socioeconomic group bias from dataset under-representation and shows it degrades care quality for vulnerable patients. Post-modeling strategies in two proposed overbooking methods fully mitigate the bias, improving fairness.","Faculty of Engineering INDUSTRIAL ENGINEERING Thesis Project - First Semester 2024  \n[233022] Machine Learning Overbooking Framework for Outpatient Appointments:  \nImproving Resource Allocation and Correcting Socioeconomic Bias  \nJosé Rafael Peña Gutiérreza,c , Santiago Ospina Ferreiraa,c , Julián Darío Romero  \nRomeroa,c  \nDavid Barrera Ferrob,c , Gabriel Zambrano Reyb,c  \na Industrial Engineering Student  \nb Professor, Thesis Director, Industrial Engineering Department c Pontificia Universidad Javeriana, Bogotá, Colombia  \nAbstract  \nOutpatient care constitutes the primary healthcare service across different countries. Appointment scheduling within this care setting faces the significant challenge of patient no-shows, which is detrimental to service quality, leading to treatment delays and economic losses for healthcare centers. With the rise of artificial intelligence, the combination of strategies such as overbooking and machine learning (ML) models has emerged as a promising approach. However, there are concerns regarding group bias (GB) and its potential to result in unfair services, perpetuating historical barriers and disparities that society is striving to eliminate.  \nIn the ML-enabled overbooking framework proposed in this study, we demonstrate the presence of socioeconomic GB due to the under-representation of a socioeconomically vulnerable population within the dataset, and how this worsens the service quality for the vulnerable group. We illustrate how including post-modeling strategies in the two proposed overbooking methodologies can completely mitigate this effect, ensuring fairness in the framework that combines overbooking and ML.  \nKeywords—Machine Learning, Algorithm Fairness, Metaheuristics, Appointment Scheduling, Simulation, Bias  \n1 Introduction  \nOutpatient care, encompassing medical procedures, tests, and consultations without overnight stays, constitutes the primary healthcare service in most developed countries, with the highest percentage of national health expenditures compared to other care types [1] . Appointment Scheduling (AS) encounters numerous challenges in these high-demand scenarios, including resource allocation, ensuring workload efficiency, and reducing patient waiting times. Operations research methods have demonstrated positive  \nimpacts in addressing these challenges [2, 3] . Implementing an outpatient appointment system is considered an indicator of high-quality service in terms of accessibility and availability for both patients and staff members [4] .  \nIn this context, patient no-shows comprise uncancelled and unrescheduled appointments in which the patient fails to attend [5] . This behavior is detrimental for outpatient care centers, leading to delays in diagnosis and initiation of treatment [6], increased premature mortality rates [7], among other consequences. Additionally, economic consequences for healthcare facilities could include idle time for both physiciansand consultancy rooms [8, 9] . In Bogotá, for example, during 2016, the Health Secretary reported losses of nearly 16,000 million pesos due to non-compliance with 422,971 medical appointments of health service users [10] .  \nTherefore, there is growing interest within the healthcare community to study this behavior and address its consequences [11] . Passive strategies, such as confirmation and reminder methods, haven’t consistently shown success across systems [9] . Consequently, more active policies such as overbooking have been incorporated into AS systems [12] . This method involves assigning more than one patient to a single appointment slot, operating on the assumption that there’s a probability that one of the patients might not show up.  \nWhile a general no-show probability is often used when designing overbooking policies, an individual no-show probability framework, considering several specific-to-patient variables, emerges as a promising approach with high potential for expansion. Some studies have highl","cbCaiqLmrwXFnIJ6","https://ap.wps.com/l/cbCaiqLmrwXFnIJ6","pdf",1437877,1,29,"English","en",105,"# Introduction\n## Outpatient appointment scheduling challenges\n## Patient no-shows and their consequences\n## Overbooking as an appointment policy\n## Machine learning for no-show prediction\n## Group bias and fairness in ML","[{\"question\":\"What problem does the proposed framework address in outpatient care?\",\"answer\":\"It addresses patient no-shows that lead to delays in diagnosis and treatment and generate economic and operational losses for healthcare centers.\"},{\"question\":\"How does the study detect socioeconomic group bias?\",\"answer\":\"It demonstrates that socioeconomic group bias exists due to the under-representation of socioeconomically vulnerable patients in the dataset, producing worse outcomes for that group.\"},{\"question\":\"What approach is used to improve fairness in the overbooking framework?\",\"answer\":\"The study applies post-modeling strategies in two overbooking methodologies to completely mitigate the bias and ensure fairness when combining overbooking with ML.\"}]","Machine Learning Overbooking Framework for Outpatient Appointments - Improving Resource Allocation and Correcting Socioeconomic Bias | PDF",1785895716,73,{"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},"machine-learning-overbooking-framework-for-outpatient-appointments-improving-resource-allocation-and-correcting-socioeconomic-bias","",{"@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/machine-learning-overbooking-framework-for-outpatient-appointments-improving-resource-allocation-and-correcting-socioeconomic-bias/124970/",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},"What problem does the proposed framework address in outpatient care?","Question",{"text":75,"@type":76},"It addresses patient no-shows that lead to delays in diagnosis and treatment and generate economic and operational losses for healthcare centers.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study detect socioeconomic group bias?",{"text":80,"@type":76},"It demonstrates that socioeconomic group bias exists due to the under-representation of socioeconomically vulnerable patients in the dataset, producing worse outcomes for that group.",{"name":82,"@type":73,"acceptedAnswer":83},"What approach is used to improve fairness in the overbooking framework?",{"text":84,"@type":76},"The study applies post-modeling strategies in two overbooking methodologies to completely mitigate the bias and ensure fairness when combining overbooking with ML.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]