A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

Shan Nan; Tianhua Tang; Hongshuo Feng; Yijie Wang; Mengyang Li; Xudong Lu; Huilong Duan

doi:10.2196/21628

A Computer-Interpretable Guideline for COVID-19: Rapid Development and Dissemination

JMIR Med Inform. 2020 Oct 1;8(10):e21628. doi: 10.2196/21628.

Authors

Shan Nan^{1

2}, Tianhua Tang¹, Hongshuo Feng¹, Yijie Wang³, Mengyang Li¹, Xudong Lu^{1

2}, Huilong Duan¹

Affiliations

¹ College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China.
² Information Systems Industrial Engineering & Innovation Sciences, Technical University of Eindhoven, Eindhoven, Netherlands.
³ Hangzhou Vico Software Cooperation, Hangzhou, China.

PMID: 32931443
PMCID: PMC7546731
DOI: 10.2196/21628

Abstract

Background: COVID-19 is a global pandemic that is affecting more than 200 countries worldwide. Efficient diagnosis and treatment are crucial to combat the disease. Computer-interpretable guidelines (CIGs) can aid the broad global adoption of evidence-based diagnosis and treatment knowledge. However, currently, no internationally shareable CIG exists.

Objective: The aim of this study was to establish a rapid CIG development and dissemination approach and apply it to develop a shareable CIG for COVID-19.

Methods: A 6-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate ambiguities during development of the CIG. The Guideline Definition Language (GDL) was used to capture the clinical rules. A CIG for COVID-19 was developed by translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline. A prototype application was implemented to validate the CIG.

Results: We used 27 archetypes for the COVID-19 guideline. We developed 18 GDL rules to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG was further translated to object data model and Drools rules to facilitate its use by people who do not employ the non-openEHR archetype. The prototype application validated the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub.

Conclusions: Our rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development. A validated COVID-19 CIG is now available to the public.

Keywords: CDSS; COVID-19; Guideline Definition Language; algorithm; development; dissemination; electronic health record; guideline; openEHR.

©Shan Nan, Tianhua Tang, Hongshuo Feng, Yijie Wang, Mengyang Li, Xudong Lu, Huilong Duan. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 01.10.2020.