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Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report
Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450955/ https://www.ncbi.nlm.nih.gov/pubmed/34535448 http://dx.doi.org/10.1136/bmjhci-2021-100447 |
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author | van de Sande, Davy Van Genderen, Michel E. Huiskens, Joost Veen, Robert E. R. Meijerink, Yvonne Gommers, Diederik van Bommel, Jasper |
author_facet | van de Sande, Davy Van Genderen, Michel E. Huiskens, Joost Veen, Robert E. R. Meijerink, Yvonne Gommers, Diederik van Bommel, Jasper |
author_sort | van de Sande, Davy |
collection | PubMed |
description | Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data. Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure. Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future. |
format | Online Article Text |
id | pubmed-8450955 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-84509552021-09-20 Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report van de Sande, Davy Van Genderen, Michel E. Huiskens, Joost Veen, Robert E. R. Meijerink, Yvonne Gommers, Diederik van Bommel, Jasper BMJ Health Care Inform Implementer Report Introduction In the current situation, clinical patient data are often siloed in multiple hospital information systems. Especially in the intensive care unit (ICU), large volumes of clinical data are routinely collected through continuous patient monitoring. Although these data often contain useful information for clinical decision making, they are not frequently used to improve quality of care. During, but also after, pressing times, data-driven methods can be used to mine treatment patterns from clinical data to determine the best treatment options from a hospitals own clinical data. Methods In this implementer report, we describe how we implemented a data infrastructure that enabled us to learn in real time from consecutive COVID-19 ICU admissions. In addition, we explain our step-by-step multidisciplinary approach to establish such a data infrastructure. Conclusion By sharing our steps and approach, we aim to inspire others, in and outside ICU walls, to make more efficient use of data at hand, now and in the future. BMJ Publishing Group 2021-09-17 /pmc/articles/PMC8450955/ /pubmed/34535448 http://dx.doi.org/10.1136/bmjhci-2021-100447 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) . |
spellingShingle | Implementer Report van de Sande, Davy Van Genderen, Michel E. Huiskens, Joost Veen, Robert E. R. Meijerink, Yvonne Gommers, Diederik van Bommel, Jasper Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
title | Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
title_full | Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
title_fullStr | Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
title_full_unstemmed | Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
title_short | Generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
title_sort | generating insights in uncharted territories: real-time learning from data in critically ill patients–an implementer report |
topic | Implementer Report |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8450955/ https://www.ncbi.nlm.nih.gov/pubmed/34535448 http://dx.doi.org/10.1136/bmjhci-2021-100447 |
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