[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122865-en":3,"doc-seo-122865-105":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},122865,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing","Post-anesthesia care unit (PACU) length of stay is a key perioperative efficiency metric, especially in outpatient ambulatory surgery centers where throughput drives cost and performance. This study developed machine learning models to predict ambulatory surgery patients at risk for prolonged PACU time using only pre-operatively identified factors, then simulated whether case resequencing could reduce after-hours PACU staffing needs. Prolonged PACU stay was defined as ≥3 hours. Several classifiers were trained and evaluated on a risk label, then historic cases were re-ordered based on predicted risk. Among 10,928 patients, 580 (5.31%) met the prolonged threshold. XGBoost with SMOTE achieved the best discrimination (AUC=0.712). Resequencing with this model improved the proportion of days with after-hours PACU presence past 7:00 pm (41% vs 12%, P\u003C0.0001), suggesting preoperative characteristics can support optimized sequencing to lessen prolonged PACU impact on staffing.","UC San Diego  \nUC San Diego Previously Published Works  \nTitle  \nMachine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing.  \nPermalink  \n[https://escholarship.org/uc/item/4w70k80p](https://escholarship.org/uc/item/4w70k80p)  \nJournal  \nJournal of Medical Systems, 47(1)  \nAuthors  \nTully, Jeffrey  \nZhong, William Simpson, Sierra et al.  \nPublication Date  \n2023-07-10  \nDOI  \n10.1007/s10916-023-01966-9  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nJournal of Medical Systems (2023) 47:71  \n[https://doi.org/10.1007/s10916-023-01966-9](https://doi.org/10.1007/s10916-023-01966-9)  \nMachine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing  \nJeffrey L. Tully1 · William Zhong · Sierra Simpson1 · Brian P. Curran1 · Alvaro A. Macias1 · Ruth S. Waterman1 ·  \nRodney A. Gabriel1,2  \nReceived: 13 February 2023 / Accepted: 2 July 2023 / Published online: 10 July 2023 © The Author(s) 2023  \nAbstract  \nThe post-anesthesia care unit (PACU) length of stay is an important perioperative efficiency metric. The aim of this study was to develop machine learning models to predict ambulatory surgery patients at risk for prolonged PACU length of stay-using only pre-operatively identified factors-and then to simulate the effectiveness in reducing the need for after-hours PACU staffing. Several machine learning classifier models were built to predict prolonged PACU length of stay (defined as PACU stay ≥ 3 hours) on a training set. A case resequencing exercise was then performed on the test set, in which historic cases were re-sequenced based on the predicted risk for prolonged PACU length of stay. The frequency of patients remaining in the PACU after-hours (≥ 7:00 pm) were compared between the simulated operating days versus actual operating room days. There were 10,928 ambulatory surgical patients included in the analysis, of which 580 (5.31%) had a PACU length of stay ≥ 3 hours. XGBoost with SMOTE performed the best (AUC = 0.712) . The case resequencing exercise utilizing the XGBoost model resulted in an over three-fold improvement in the number of days in which patients would be in the PACU past 7pm as compared with historic performance (41% versus 12%, P\u003C0.0001). Predictive models using preoperative patient characteristics may allow for optimized case sequencing, which may mitigate the effects of prolonged PACU lengths of stay on after-hours staffing utilization.  \nKeywords Perioperative resource management · Outpatient surgery · Machine learning · Artificial intelligence · Perioperative informatics  \nIntroduction  \nPost-anesthesia care unit (PACU) length of stay (LOS) is an important focus of efforts to improve quality and decrease costs of perioperative care, particularly in the outpatient surgery center where patient throughput is a key determinant of efficiency and related financial metrics [1, 2] . The issues associated with prolonged PACU stay (especially when the stay occurs after-hours in a freestanding ambulatory surgery center) include increased risk for hospital admission, decreased patient satisfaction, and increased staffing and  \n* Jeffrey L. Tully [jtully@health.ucsd.edu](jtully@health.ucsd.edu)  \n1 Department of Anesthesiology, Division of Perioperative Informatics, University of California, San Diego, La Jolla, CA, USA  \n2 Department of Medicine, Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA  \noperational costs [3–8]. Optimizing the sequencing of surgical case order in an operating room may aid in reducing PACU usage after-hours (e.g. patients predicted to have the longest PACU stays could be rescheduled to occur earlier in the day) .  \nThe development of predictive models for prolonged PACU LOS could be clinically useful in the optimization of case order sequencing with the goal of reducing after","cbCaiucnPueZfXgo","https://ap.wps.com/l/cbCaiucnPueZfXgo","pdf",1133850,1,10,"English","en",105,"# Abstract\n# Introduction\n# Methods","[{\"question\":\"What does the study aim to achieve regarding PACU workflow?\",\"answer\":\"It aims to predict patients at risk for prolonged PACU length of stay and then simulate case resequencing to reduce the need for after-hours PACU staffing.\"},{\"question\":\"How is prolonged PACU length of stay defined in the analysis?\",\"answer\":\"Prolonged PACU length of stay is defined as PACU stay duration of at least 3 hours (PACU stay ≥ 3 hours).\"},{\"question\":\"Which model performed best, and what improvement did it produce in the simulation?\",\"answer\":\"XGBoost with SMOTE performed best (AUC=0.712). Using this model for case resequencing produced a more than three-fold improvement in days with patients remaining in the PACU past 7:00 pm (41% vs 12%, P\\u003c0.0001).\"}]","Machine Learning Prediction Models to Reduce Length of Stay at Ambulatory Surgery Centers Through Case Resequencing | PDF",1785813414,25,{"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-prediction-models-to-reduce-length-of-stay-at-ambulatory-surgery-centers-through-case-resequencing","",{"@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/machine-learning-prediction-models-to-reduce-length-of-stay-at-ambulatory-surgery-centers-through-case-resequencing/122865/",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","2026-08-04",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 does the study aim to achieve regarding PACU workflow?","Question",{"text":75,"@type":76},"It aims to predict patients at risk for prolonged PACU length of stay and then simulate case resequencing to reduce the need for after-hours PACU staffing.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is prolonged PACU length of stay defined in the analysis?",{"text":80,"@type":76},"Prolonged PACU length of stay is defined as PACU stay duration of at least 3 hours (PACU stay ≥ 3 hours).",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best, and what improvement did it produce in the simulation?",{"text":84,"@type":76},"XGBoost with SMOTE performed best (AUC=0.712). Using this model for case resequencing produced a more than three-fold improvement in days with patients remaining in the PACU past 7:00 pm (41% vs 12%, P\u003C0.0001).","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]