Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jun 21, 2020
Date Accepted: Sep 13, 2020
Date Submitted to PubMed: Sep 15, 2020
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Rapid Development and Dissemination of a Computer Interpretable Guideline for COVID-19
ABSTRACT
Background:
Coronavirus disease 2019 (COVID-19) is a global pandemic affecting more than 200 counties. Efficient diagnosis and effective treatment are crucial to combat the disease. Computer interpretable guidelines (CIG) can help the broad adoption of evidence-based diagnosis and treatment knowledge globally. However, there is currently a lack of an internationally shareable CIG due to the difficulty of guideline development.
Objective:
This study contributes a rapid CIG development and dissemination approach and developed a shareable CIG for COVID-19.
Methods:
A six-step rapid CIG development and dissemination approach was designed and applied. Processes, roles, and deliverable artifacts were specified in this approach to eliminate the ambiguities during CIG development. Guideline definition language (GDL) was used to capture the clinical rules. By translating, interpreting, annotating, extracting, and formalizing the Chinese COVID-19 diagnosis and treatment guideline, a CIG for COVID-19 was developed. A prototype application was implemented to validate the CIG.
Results:
27 archetypes have been used for the COVID-19 guideline. 18 GDL rules were developed to cover the diagnosis and treatment suggestion algorithms in the narrative guideline. The CIG is further translated to object data model and Drools rules to facilitate the use of non-openEHR users. The prototype application validates the correctness of the CIG with a public data set. Both the GDL rules and Drools rules have been disseminated on GitHub.
Conclusions:
The proposed rapid CIG development and dissemination approach accelerated the pace of COVID-19 CIG development.
Citation
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