[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126696-en":3,"doc-seo-126696-105":30,"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":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},126696,962084925636,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Healthcare provider evaluation of machine learning-directed care - reactions to deployment on a randomised controlled study","Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust, and few prospective studies evaluate provider concerns. A randomised controlled study, SHIELD-RT (NCT03775265), previously showed ML could accurately direct clinical evaluations to reduce acute care during cancer radiotherapy. This report characterises subsequent perceptions and barriers to implementation. An anonymous 7-item Likert survey with optional free text was completed by multidisciplinary staff.","UCSF  \nUC San Francisco Previously Published Works  \nTitle  \nHealthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study  \nPermalink  \n[https://escholarship.org/uc/item/5g5278wk](https://escholarship.org/uc/item/5g5278wk)  \nJournal  \nBMJ Health & Care Informatics Online, 30(1)  \nISSN  \n2058-4555  \nAuthors  \nHong, Julian C  \nPatel, Pranalee Eclov, Neville CWet al.  \nPublication Date  \n2023-02-01  \nDOI  \n10.1136/bmjhci-2022-100674  \nCopyright Information  \nThis work is made available under the terms of a Creative Commons Attribution  \nNonCommercial License, availalbe at [https://creativecommons.org/licenses/by-nc/4.0/](https://creativecommons.org/licenses/by-nc/4.0/)  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nOpen access Implementer report  \nHealthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study  \nJulian C Hong,1,2,3 Pranalee Patel,4 Neville C W Eclov,4 Sarah J Stephens,4 Yvonne M Mowery,4,5 Jessica D Tenenbaum,6 Manisha Palta4  \nTo cite: Hong JC, Patel P, Eclov NCW, et al. Healthcare provider evaluation of machine learning-directed care: reactions to deployment on a randomised controlled study. BMJ Health Care Inform 2023;30:e100674 . doi:10 . 1136/ bmjhci-2022-100674  \nReceived 01 September 2022 Accepted 28 January 2023  \n© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.  \n1Department of Radiation Oncology, University of California San Francisco, San Francisco, California, USA  \n2Bakar Computational Health Sciences Institute, University of California San Francisco, San Francisco, California, USA 3Joint Program in Computational Precision Health, UCSF-UC Berkeley, San Francisco, California, USA  \n4Department of Radiation Oncology, Duke University, Durham, North Carolina, USA 5Department of Head and Neck Surgery & Communication Sciences, Duke University, Durham, North Carolina, USA 6Department of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina, USA  \nCorrespondence to  \nDr Julian C Hong; [julian.hong@ucsf.edu](julian.hong@ucsf.edu)  \nABSTRACT  \nObjectives Clinical artificial intelligence and machine learning (ML) face barriers related to implementation and trust. There have been few prospective opportunities to evaluate these concerns. System for High Intensity EvaLuation During Radiotherapy (NCT03775265) was a randomised controlled study demonstrating that ML accurately directed clinical evaluations to reduce acute care during cancer radiotherapy. We characterised subsequent perceptions and barriers to implementation. Methods An anonymous 7-question Likert-type scale survey with optional free text was administered to multidisciplinary staff focused on workflow, agreement with ML and patient experience.  \nResults 59/71 (83%) responded. 81% disagreed/ strongly disagreed their workflow was disrupted. 67% agreed/strongly agreed patients undergoing intervention were high risk. 75% agreed/strongly agreed they would implement the ML approach routinely if the study was positive. Free-text feedback focused on patient education and ML predictions.  \nConclusions Randomised data and firsthand experience support positive reception of clinical ML. Providers highlighted future priorities, including patient counselling and workflow optimisation.  \nINTRODUCTION  \nArtificial intelligence (AI) and machine learning (ML) has the potential to transform medical practice. Despite many retrospective studies, randomised controlled trials (RCTs), particularly interventional trials, remain limited.1–3 Thus, there have been limited opportunities to formally characterise barriers to the implementation of healthcare AI and ML and identify solutions.4 5 There are minimal reports describing provider opinions following a prospective ran","cbCaipek6JnLeiLd","https://ap.wps.com/l/cbCaipek6JnLeiLd","pdf",246656,1,4,"English","en",105,"# Abstract\n# Introduction\n# Methods\n# Results\n# Conclusions","[{\"question\":\"What barriers to ML implementation and trust were addressed in this study?\",\"answer\":\"The study focuses on concerns related to implementation and trust in clinical AI and machine learning, and examines provider perspectives after deployment.\"},{\"question\":\"How was the ML strategy evaluated and deployed in the underlying trial?\",\"answer\":\"The SHIELD-RT randomised controlled study used an ML model to identify high-risk patients during radiotherapy, directing clinical evaluations to reduce acute care.\"},{\"question\":\"Who participated in the perceptions survey and what was measured?\",\"answer\":\"Multidisciplinary staff completed an anonymous 7-question Likert-type survey with optional free-text, addressing workflow, agreement with ML, and patient experience.\"},{\"question\":\"What future priorities did providers highlight after the study?\",\"answer\":\"Providers emphasized needs related to patient counselling and workflow optimisation for routine implementation.\"}]","Healthcare provider evaluation of machine learning-directed care - 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