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Rapid translation of clinical guidelines into executable knowledge : a case study of COVID-19 and online demonstration
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Fox, John, Khan, Omar, Curtis, Hywel, Wright, Andrew, Pal, Carla, Cockburn, Neil, Cooper, Jennifer, Chandan, Joht S. and Nirantharakumar, Krishnarajah (2021) Rapid translation of clinical guidelines into executable knowledge : a case study of COVID-19 and online demonstration. Learning Health Systems, 5 (1). e10236. doi:10.1002/lrh2.10236 ISSN 2379-6146.
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WRAP-rapid-translation-clinical-guidelines-executable-knowledge-case-study-COVID‐19-online-demonstration-Khan-2020.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (1044Kb) | Preview |
Official URL: http://dx.doi.org/10.1002/lrh2.10236
Abstract
Introduction:
We report a pathfinder study of AI/knowledge engineering methods to rapidly formalise COVID‐19 guidelines into an executable model of decision making and care pathways. The knowledge source for the study was material published by BMJ Best Practice in March 2020.
Methods:
The PROforma guideline modelling language and OpenClinical.net authoring and publishing platform were used to create a data model for care of COVID‐19 patients together with executable models of rules, decisions and plans that interpret patient data and give personalised care advice.
Results:
PROforma and OpenClinical.net proved to be an effective combination for rapidly creating the COVID‐19 model; the Pathfinder 1 demonstrator is available for assessment at https://www.openclinical.net/index.php?id=746.
Conclusions:
This is believed to be the first use of AI/knowledge engineering methods for disseminating best‐practice in COVID‐19 care. It demonstrates a novel and promising approach to the rapid translation of clinical guidelines into point of care services, and a foundation for rapid learning systems in many areas of healthcare.
Item Type: | Journal Article | ||||||||
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Subjects: | Q Science > Q Science (General) R Medicine > R Medicine (General) R Medicine > RA Public aspects of medicine |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||||
Library of Congress Subject Headings (LCSH): | COVID-19 (Disease), Artificial intelligence, COVID-19 (Disease) -- Data processing, Medical informatics | ||||||||
Journal or Publication Title: | Learning Health Systems | ||||||||
Publisher: | John Wiley & Sons, Inc. | ||||||||
ISSN: | 2379-6146 | ||||||||
Official Date: | 15 January 2021 | ||||||||
Dates: |
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Volume: | 5 | ||||||||
Number: | 1 | ||||||||
Article Number: | e10236 | ||||||||
DOI: | 10.1002/lrh2.10236 | ||||||||
Status: | Peer Reviewed | ||||||||
Publication Status: | Published | ||||||||
Access rights to Published version: | Open Access (Creative Commons) | ||||||||
Date of first compliant deposit: | 20 July 2020 | ||||||||
Date of first compliant Open Access: | 20 July 2020 |
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