tetano
Editor, Senior Moderator
JMIR Med Inform
. 2020 Sep 13.
doi: 10.2196/21628. Online ahead of print.
A Computer Interpretable Guideline for COVID-19: Rapid Development and Dissemination
Shan Nan[SUP] 1 2 [/SUP], Tianhua Tang[SUP] 1 [/SUP], Hongshuo Feng[SUP] 1 [/SUP], Yijie Wang[SUP] 3 [/SUP], Mengyang Li[SUP] 1 [/SUP], Xudong Lu[SUP] 1 [/SUP], Huilong Duan[SUP] 1 [/SUP]
Affiliations
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.
Objective: This study aims to contribute a rapid CIG development and dissemination approach and develop 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. A validated COVID-19 CIG is now available for the public.
. 2020 Sep 13.
doi: 10.2196/21628. Online ahead of print.
A Computer Interpretable Guideline for COVID-19: Rapid Development and Dissemination
Shan Nan[SUP] 1 2 [/SUP], Tianhua Tang[SUP] 1 [/SUP], Hongshuo Feng[SUP] 1 [/SUP], Yijie Wang[SUP] 3 [/SUP], Mengyang Li[SUP] 1 [/SUP], Xudong Lu[SUP] 1 [/SUP], Huilong Duan[SUP] 1 [/SUP]
Affiliations
- PMID: 32931443
- DOI: 10.2196/21628
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.
Objective: This study aims to contribute a rapid CIG development and dissemination approach and develop 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. A validated COVID-19 CIG is now available for the public.