[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128664-en":3,"doc-seo-128664-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},128664,962084925782,"Ava Thompson","https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc",8,"Research & Report","Supporting long-term condition management - a workflow framework for the co-development and operationalization of machine learning models using electronic health record data insights","Long-term conditions including cardiovascular disease, COPD, asthma, and diabetes are increasing and drive avoidable mortality, hospital admissions, and healthcare costs. Machine learning can enable earlier diagnosis, triage, and treatment selection, yet translation into live clinical practice remains limited due to insufficient clinical involvement and planning beyond initial model development. The work proposes a multistage workflow framework to support coordinated co-development and operationalization of machine learning models using routine electronic health record data, illustrated via two risk-prediction model case studies for COPD.","TYPE Methods  \nPUBLISHED 12 November 2024 DOI 10.3389/frai.2024.1458508  \nOPEN ACCESS  \nEDITED BY  \nFarah Kidwai-Khan,  \nYale University, United States  \nREVIEWED BY  \nArlene Casey,  \nUniversity of Edinburgh, United Kingdom Balu Bhasuran,  \nFlorida State University, United States  \n*CORRESPONDENCE  \nShane Burns  \n [shane.burns@lenushealth.com](shane.burns@lenushealth.com)  \nRECEIVED 02 July 2024  \nACCEPTED 29 October 2024  \nPUBLISHED 12 November 2024  \nCITATION  \nBurns S, Cushing A, Taylor A, Lowe DJ and Carlin C (2024) Supporting long-term condition management: a workflow framework for the co-development andoperationalization of machine learning models using electronic health record data insights.  \nFront. Artif. Intell. 7:1458508 .  \ndoi: 10.3389/frai.2024.1458508  \nCOPYRIGHT  \n© 2024 Burns, Cushing, Taylor, Lowe and Carlin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nSupporting long-term condition management: a workflow framework for the  \nco-development andoperationalization of machine learning models using electronic health record data insights  \nShane Burns 1*, Andrew Cushing 1, Anna Taylor 2, David J. Lowe 2 and Christopher Carlin 2  \n1 Lenus Health Ltd., Edinburgh, United Kingdom, 2 Departments of Respiratory and Emergency Medicine, Queen Elizabeth University Hospital, NHS Greater Glasgow and Clyde, Glasgow, United Kingdom  \nThe prevalence of long-term conditions such as cardiovascular disease, chronic obstructive pulmonary disease (COPD), asthma, and diabetes mellitus is rising. These conditions are leading sources of premature mortality, hospital admission, and healthcare expenditure. Machine learning approaches to improve the management of these conditions have been widely explored, with datadriven insights demonstrating the potential to support earlier diagnosis, triage, and treatment selection. The translation of this research into tools used in live clinical practice has however been limited, with many projects lacking clinical involvement and planning beyond the initial model development stage. To support the move toward a more coordinated and collaborative working process from concept to investigative use in a live clinical environment, we present a multistage workflow framework for the co-development and operationalization of machine learning models which use routine clinical data derived from electronic health records. The approach outlined in this framework has been informed by our multidisciplinary team’s experience of co-developing and operationalizing risk prediction models for COPD within NHS Greater Glasgow & Clyde. In this paper, we provide a detailed overview of this framework, alongside a description of the development and operationalization of two of these risk-prediction models as case studies of this approach.  \nKEYWORDS  \nmachine learning in healthcare, long-term condition management, risk prediction, model operationalization, electronic health record data, workflow framework  \n1 Introduction  \nThe NHS and other healthcare systems face various long-term challenges, many of which have been exacerbated by the impacts ofthe COVID-19 pandemic on existing financial strainsand waiting list backlogs (Cutler, 2022; Khan, 2016). A key issue is the increasing number of individuals affected by long-term conditions (Atella et al., 2019; Holman, 2020). Long-term conditions (often referred to as chronic conditions) are defined as conditions which cannot  \n[Frontiers in](Frontiers in Artificial Intelligence 01 frontiersin.org)[ Artificial Intelligence](Frontiers in Artificial Intelligence 01 frontiersin.org)[ 0","cbCaiv5ErR9jOEqG","https://ap.wps.com/l/cbCaiv5ErR9jOEqG","pdf",11075712,1,25,"English","en",105,"# Introduction\n## Long-term conditions and healthcare system pressures\n## Machine learning potential and barriers to clinical translation\n# Multistage workflow framework\n## Co-development to operationalization in live settings\n## Case studies: COPD risk-prediction models","[{\"question\":\"Why is supporting long-term condition management important?\",\"answer\":\"Long-term conditions are rising and contribute to premature mortality, hospital admissions, and substantial healthcare expenditure, making proactive management essential.\"},{\"question\":\"What limits the use of machine learning models in routine clinical practice?\",\"answer\":\"Many projects lack sustained clinical involvement and planning beyond the initial model development stage, reducing translation into operational tools.\"},{\"question\":\"What does the proposed workflow framework focus on?\",\"answer\":\"It provides a multistage process to co-develop and operationalize machine learning models using routine electronic health record data, progressing from concept to investigative use in live clinical environments.\"}]","Supporting long-term condition management - 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