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A data-driven framework for clinical decision support applied to pneumonia management

Despite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important tha...

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Autores principales: Free, Robert C., Lozano Rojas, Daniel, Richardson, Matthew, Skeemer, Julie, Small, Leanne, Haldar, Pranabashis, Woltmann, Gerrit
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591306/
https://www.ncbi.nlm.nih.gov/pubmed/37877124
http://dx.doi.org/10.3389/fdgth.2023.1237146
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author Free, Robert C.
Lozano Rojas, Daniel
Richardson, Matthew
Skeemer, Julie
Small, Leanne
Haldar, Pranabashis
Woltmann, Gerrit
author_facet Free, Robert C.
Lozano Rojas, Daniel
Richardson, Matthew
Skeemer, Julie
Small, Leanne
Haldar, Pranabashis
Woltmann, Gerrit
author_sort Free, Robert C.
collection PubMed
description Despite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important that the users can understand and visualise the reasons for the decision support predictions. Plausibility can be hard to achieve for complex pathways and models and perceived “black-box” functionality often leads to a lack of trust. Here, we describe and evaluate a data-driven framework which moderates some of these issues and demonstrate its applicability to the in-hospital management of community acquired pneumonia, an acute respiratory disease which is a leading cause of in-hospital mortality world-wide. We use the framework to develop and test a clinical decision support tool based on local guideline aligned management of the disease and show how it could be used to effectively prioritise patients using retrospective analysis. Furthermore, we show how this tool can be embedded into a prototype clinical system for disease management by integrating metrics and visualisations. This will assist decision makers to examine complex patient journeys, risk scores and predictions from embedded machine learning and artificial intelligence models. Our results show the potential of this approach for developing, testing and evaluating workflow based clinical decision support tools which include complex models and embedding them into clinical systems.
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spelling pubmed-105913062023-10-24 A data-driven framework for clinical decision support applied to pneumonia management Free, Robert C. Lozano Rojas, Daniel Richardson, Matthew Skeemer, Julie Small, Leanne Haldar, Pranabashis Woltmann, Gerrit Front Digit Health Digital Health Despite their long history, it can still be difficult to embed clinical decision support into existing health information systems, particularly if they utilise machine learning and artificial intelligence models. Moreover, when such tools are made available to healthcare workers, it is important that the users can understand and visualise the reasons for the decision support predictions. Plausibility can be hard to achieve for complex pathways and models and perceived “black-box” functionality often leads to a lack of trust. Here, we describe and evaluate a data-driven framework which moderates some of these issues and demonstrate its applicability to the in-hospital management of community acquired pneumonia, an acute respiratory disease which is a leading cause of in-hospital mortality world-wide. We use the framework to develop and test a clinical decision support tool based on local guideline aligned management of the disease and show how it could be used to effectively prioritise patients using retrospective analysis. Furthermore, we show how this tool can be embedded into a prototype clinical system for disease management by integrating metrics and visualisations. This will assist decision makers to examine complex patient journeys, risk scores and predictions from embedded machine learning and artificial intelligence models. Our results show the potential of this approach for developing, testing and evaluating workflow based clinical decision support tools which include complex models and embedding them into clinical systems. Frontiers Media S.A. 2023-10-09 /pmc/articles/PMC10591306/ /pubmed/37877124 http://dx.doi.org/10.3389/fdgth.2023.1237146 Text en © 2023 Free, Lozano Rojas, Richardson, Skeemer, Small, Haldar and Woltmann. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/) . 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.
spellingShingle Digital Health
Free, Robert C.
Lozano Rojas, Daniel
Richardson, Matthew
Skeemer, Julie
Small, Leanne
Haldar, Pranabashis
Woltmann, Gerrit
A data-driven framework for clinical decision support applied to pneumonia management
title A data-driven framework for clinical decision support applied to pneumonia management
title_full A data-driven framework for clinical decision support applied to pneumonia management
title_fullStr A data-driven framework for clinical decision support applied to pneumonia management
title_full_unstemmed A data-driven framework for clinical decision support applied to pneumonia management
title_short A data-driven framework for clinical decision support applied to pneumonia management
title_sort data-driven framework for clinical decision support applied to pneumonia management
topic Digital Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10591306/
https://www.ncbi.nlm.nih.gov/pubmed/37877124
http://dx.doi.org/10.3389/fdgth.2023.1237146
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