Rapid translation of clinical guidelines into executable knowledge: a case study of COVID-19 and online demonstration

John Fox; Omar Khan; Hywel Curtis; Andrew Wright; Carla Pal; Neil Cockburn; Jennifer Cooper; Joht S. Chandan; Krishnarajah Nirantharakumar

doi:10.1002/lrh2.10236

Rapid translation of clinical guidelines into executable knowledge: a case study of COVID-19 and online demonstration

John Fox^*
, Omar Khan
, Hywel Curtis
, Andrew Wright
, Carla Pal
, Neil Cockburn
, Jennifer Cooper
, Joht S. Chandan
, Krishnarajah Nirantharakumar

^*Corresponding author for this work

Applied Health Sciences

Research output: Contribution to journal › Article › peer-review

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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.

Original language	English
Article number	e10236
Number of pages	7
Journal	Learning Health Systems
Volume	5
Issue number	1
Early online date	18 Jun 2020
DOIs	https://doi.org/10.1002/lrh2.10236
Publication status	Published - Jan 2021

Keywords

artificial intelligence
COVID-19
rapid learning systems

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FoxJ2020RapidFinal published version, 1 MBLicence: Creative Commons: Attribution (CC BY)

Cite this

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title = "Rapid translation of clinical guidelines into executable knowledge: a case study of COVID-19 and online demonstration",

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.",

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AU - Pal, Carla

AU - Cockburn, Neil

AU - Cooper, Jennifer

AU - Chandan, Joht S.

AU - Nirantharakumar, Krishnarajah

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N2 - 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.

AB - 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.

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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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Keywords

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