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.
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.
Original language | English |
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Article number | e10236 |
Number of pages | 7 |
Journal | Learning Health Systems |
Volume | 5 |
Issue number | 1 |
Early online date | 18 Jun 2020 |
DOIs | |
Publication status | Published - Jan 2021 |
Keywords
- artificial intelligence
- COVID-19
- rapid learning systems