[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123679-en":3,"doc-seo-123679-105":29,"detail-sidebar-cat-0-en-105":94},{"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":11,"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},123679,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning","Lumbar spinal stenosis is a prevalent degenerative condition in older adults that often necessitates spinal surgery and creates substantial economic burden. Prior authorization for surgical candidates is required for coverage by health plans and is commonly approved through medical directors’ subjective, clinician-specific judgments. A machine learning model is developed using demographic factors, prior treatment history, symptoms, physical examination results, and imaging findings, trained on medical vignettes reviewed by medical directors. Results show predictive performance comparable to the panel approach, supporting automation of prior authorization decisions.","Original Article  \nDetermining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning  \nGlobal Spine Journal 2024, Vol. 14(6) 1753–1759 © The Author(s) 2023  \nArticle reuse guidelines:  \n[sagepub.com/journals-permissions](sagepub.com/journals-permissions)  \n[DOI: 10.1177/21925682231155844](DOI: 10.1177/21925682231155844)[ ](DOI: 10.1177/21925682231155844)[journals.sagepub.com/home/gsj](journals.sagepub.com/home/gsj)  \nAmaury De Barros 1,2 􀀁, Frederik Abel3 􀀁, Serhii Kolisnyk4 􀀁, Gaspere C. Geraci5, Fred Hill5, Mary Engrav5, Sundara Samavedi5, Olga Suldina6, Jack Kim5, Andrej Rusakov5,  \nDarren R. Lebl3, and Raphael Mourad5,7 􀀁  \nAbstract  \nStudy Design: Medical vignettes.  \nObjectives: Lumbar spinal stenosis (LSS) is a degenerative condition with a high prevalence in the elderly population, that is associated with a significant economic burden and often requires spinal surgery. Prior authorization of surgical candidates is required before patients can be covered by a health plan and must be approved by medical directors (MDs), which is often subjective and clinician specific. In this study, we hypothesized that the prediction accuracy of machine learning (ML) methods regarding surgical candidates is comparable to that of a panel of MDs.  \nMethods: Based on patient demographic factors, previous therapeutic history, symptoms and physical examinations and imaging findings, we propose an ML which computes the probability of spinal surgical recommendations for LSS. The model implements a random forest model trained from medical vignette data reviewed by MDs. Sets of 400 and 100 medical vignettes reviewed by MDs were used for training and testing.  \nResults: The predictive accuracy of the machine learning model was with a root mean square error (RMSE) between model predictions and ground truth of .1123, while the average RMSE between individual MD’s recommendations and ground truth was .2661 . For binary classification, the AUROC and Cohen’s kappa were .959 and .801, while the corresponding average metrics based on individual MD’s recommendations were .844 and .564, respectively.  \nConclusions: Our results suggest that ML can be used to automate prior authorization approval of surgery for LSS with performance comparable to a panel of MDs.  \nKeywords  \nLumbar spinal stenosis, spinal surgery, artificial intelligence, machine learning, surgical decision making  \n1 Toulouse NeuroImaging Center (ToNIC), University of Toulouse Paul Sabatier-INSERM, Toulouse, France  \n2 Neuroscience (Neurosurgery) Center, Toulouse University Hospital, Toulouse, France  \n3 Hospital for Special Surgery, New York, NY, USA  \n4 Vinnitsa National Medical University, Vinnytsia, Ukraine  \n5 Remedy Logic, New York, NY, USA  \n6 Cadabra Studio, Dnipro, Ukraine  \n7 University of Toulouse, Toulouse, France  \nCorresponding Author:  \nRaphael Mourad, PhD, Universit Toulouse III Paul Sabatier, 118 Rte de Narbonne, Toulouse 31062, France. Email: [raphael.mourad@univ-tlse3.fr](raphael.mourad@univ-tlse3.fr)  \nCreative Commons Non Commercial No Derivs CC BY-NC-ND: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License ([https://creativecommons.org/l](https://creativecommons.org/l)icenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access pages ([https://us.sagepub.com/en-us/nam/open-access-at-sage](https://us.sagepub.com/en-us/nam/open-access-at-sage)).  \nIntroduction  \nLumbar degenerative spine disease (DSD) is increasing in developed countries that is linked to multiple factors such as ageing of the population, sedentary lifestyle, or overweight. Among the spectrum of DSD, lumbar spinal stenosis (LSS) represents a condition that has a high incidence estimated between 1700 and 2200 per 100 000 inhabitants i","cbCainzOHgyaODTL","https://ap.wps.com/l/cbCainzOHgyaODTL","pdf",1250394,1,"English","en",105,"# Abstract\n## Study design\n## Objectives\n## Methods\n## Results\n## Conclusions\n# Introduction","[{\"question\":\"为什么腰椎管狭窄手术需要事先授权审批？\",\"answer\":\"因为在健康计划或保险覆盖前，患者的手术申请必须经过医疗总监（MD）的事先授权审核；该过程通常旨在平衡安全性与成本效益。\"},{\"question\":\"本研究使用了哪些数据来训练机器学习模型？\",\"answer\":\"模型基于患者的人口学因素、既往治疗史、症状与体格检查结果、以及影像学发现来计算对手术建议的推荐概率。\"},{\"question\":\"机器学习模型的预测效果如何？\",\"answer\":\"研究报告显示，模型预测与真实结果之间的RMSE为0.1123；二分类任务的AUROC与Cohen’s kappa分别为0.959和0.801，整体表现接近医疗总监小组水平。\"},{\"question\":\"研究结论对临床审批流程意味着什么？\",\"answer\":\"结果表明，机器学习可用于以与医疗总监小组相当的性能实现腰椎管狭窄手术的事先授权审批自动化。\"}]","Determining Prior Authorization Approval for Lumbar Stenosis Surgery With Machine Learning | PDF",1785817967,18,{"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":89,"head_meta":91,"extra_data":93,"updated_unix":27},"determining-prior-authorization-approval-for-lumbar-stenosis-surgery-with-machine-learning","",{"@graph":35,"@context":88},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"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":49},"https://docshare.wps.com/document/healthcare/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/determining-prior-authorization-approval-for-lumbar-stenosis-surgery-with-machine-learning/123679/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80,84],{"name":71,"@type":72,"acceptedAnswer":73},"为什么腰椎管狭窄手术需要事先授权审批？","Question",{"text":74,"@type":75},"因为在健康计划或保险覆盖前，患者的手术申请必须经过医疗总监（MD）的事先授权审核；该过程通常旨在平衡安全性与成本效益。","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"本研究使用了哪些数据来训练机器学习模型？",{"text":79,"@type":75},"模型基于患者的人口学因素、既往治疗史、症状与体格检查结果、以及影像学发现来计算对手术建议的推荐概率。",{"name":81,"@type":72,"acceptedAnswer":82},"机器学习模型的预测效果如何？",{"text":83,"@type":75},"研究报告显示，模型预测与真实结果之间的RMSE为0.1123；二分类任务的AUROC与Cohen’s kappa分别为0.959和0.801，整体表现接近医疗总监小组水平。",{"name":85,"@type":72,"acceptedAnswer":86},"研究结论对临床审批流程意味着什么？",{"text":87,"@type":75},"结果表明，机器学习可用于以与医疗总监小组相当的性能实现腰椎管狭窄手术的事先授权审批自动化。","https://schema.org",{"og:url":51,"og:type":90,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":92,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":95},[96,100,104,108,113,118,121,126,131,134,138],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":105,"show_sort_weight":106,"slug":107},"Exam",70,"exam",{"id":109,"doc_module":4,"doc_module_name":45,"category_name":110,"show_sort_weight":111,"slug":112},5,"Comic",60,"comic",{"id":114,"doc_module":4,"doc_module_name":45,"category_name":115,"show_sort_weight":116,"slug":117},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":119,"slug":120},40,"healthcare",{"id":122,"doc_module":4,"doc_module_name":45,"category_name":123,"show_sort_weight":124,"slug":125},8,"Research & Report",30,"research-report",{"id":127,"doc_module":4,"doc_module_name":45,"category_name":128,"show_sort_weight":129,"slug":130},9,"Religion & Spirituality",20,"religion-spirituality",{"id":129,"doc_module":4,"doc_module_name":45,"category_name":132,"show_sort_weight":129,"slug":133},"World Cup","world-cup",{"id":135,"doc_module":4,"doc_module_name":45,"category_name":136,"show_sort_weight":135,"slug":137},10,"Lifestyle","lifestyle",{"id":139,"doc_module":4,"doc_module_name":45,"category_name":140,"show_sort_weight":109,"slug":141},19,"General","general"]