Rapid translation of clinical guidelines into executable knowledge: A case study
of COVID-19 and online demonstration
#MMPMID32838035
Fox J
; Khan O
; Curtis H
; Wright A
; Pal C
; Cockburn N
; Cooper J
; Chandan JS
; Nirantharakumar K
Learn Health Syst
2021[Jan]; 5
(1
): e10236
PMID32838035
show ga
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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