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

• Main Authors: John Fox (Author), Omar Khan (Author), Hywel Curtis (Author), Andrew Wright (Author), Carla Pal (Author), Neil Cockburn (Author), Jennifer Cooper (Author), Joht S. Chandan (Author), Krishnarajah Nirantharakumar (Author)
• Format: Book
• Published: Wiley, 2021-01-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2379-6146; 10.1002/lrh2.10236
• Keywords: artificial intelligence; COVID‐19; rapid learning systems; Medicine (General); R5-920; Public aspects of medicine; RA1-1270
• Source: Learning Health Systems, Vol 5, Iss 1, Pp n/a-n/a (2021)

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.