Evidence map›Paper›PMID 32496207›Full record

ArticleJournal of medical Internet research2020

Development of an openEHR Template for COVID-19 Based on Clinical Guidelines.

Mengyang Li, Heather Leslie, Bin Qi, Shan Nan, Hongshuo Feng, Hailing Cai, Xudong Lu, Huilong Duan

Open access · goldAbstract read
In one paragraph

Article in Journal of medical Internet research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed
3.8field-weighted citation impact, top 5% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

10 citing papers in PubMed, 32 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 2 institutions in 3 countries.

Mengyang LiCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.ORCID 0000-0003-2302-4884
Heather LeslieAtomica Informatics, Melbourne, Australia.ORCID 0000-0003-3510-0522
Bin QiHangzhou Joyrun Medical Technology Cooperation, Hangzhou, Zhejiang, China.ORCID 0000-0002-7363-7489
Shan NanCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.ORCID 0000-0002-7807-3125
Hongshuo FengCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.ORCID 0000-0002-6719-487X
Hailing CaiCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.ORCID 0000-0001-7409-8547
Xudong LuCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.ORCID 0000-0001-7658-5250
Huilong DuanCollege of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, Zhejiang, China.ORCID 0000-0003-3893-213X
Ministry of Education of the People's Republic of China · CNOpen Geospatial Consortium · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe coronavirus disease (COVID-19) was discovered in China in December 2019. It has developed into a threatening international public health emergency. With the exception of China, the number of cases continues to increase worldwide. A number of studies about disease diagnosis and treatment have been carried out, and many clinically proven effective results have been achieved. Although information technology can improve the transferring of such knowledge to clinical practice rapidly, data interoperability is still a challenge due to the heterogeneous nature of hospital information systems. This issue becomes even more serious if the knowledge for diagnosis and treatment is updated rapidly as is the case for COVID-19. An open, semantic-sharing, and collaborative-information modeling framework is needed to rapidly develop a shared data model for exchanging data among systems. openEHR is such a framework and is supported by many open software packages that help to promote information sharing and interoperability.

objectiveThis study aims to develop a shared data model based on the openEHR modeling approach to improve the interoperability among systems for the diagnosis and treatment of COVID-19.

methodsThe latest Guideline of COVID-19 Diagnosis and Treatment in China was selected as the knowledge source for modeling. First, the guideline was analyzed and the data items used for diagnosis and treatment, and management were extracted. Second, the data items were classified and further organized into domain concepts with a mind map. Third, searching was executed in the international openEHR Clinical Knowledge Manager (CKM) to find the existing archetypes that could represent the concepts. New archetypes were developed for those concepts that could not be found. Fourth, these archetypes were further organized into a template using Ocean Template Editor. Fifth, a test case of data exchanging between the clinical data repository and clinical decision support system based on the template was conducted to verify the feasibility of the study.

resultsA total of 203 data items were extracted from the guideline in China, and 16 domain concepts (16 leaf nodes in the mind map) were organized. There were 22 archetypes used to develop the template for all data items extracted from the guideline. All of them could be found in the CKM and reused directly. The archetypes and templates were reviewed and finally released in a public project within the CKM. The test case showed that the template can facilitate the data exchange and meet the requirements of decision support.

conclusionsThis study has developed the openEHR template for COVID-19 based on the latest guideline from China using openEHR modeling methodology. It represented the capability of the methodology for rapidly modeling and sharing knowledge through reusing the existing archetypes, which is especially useful in a new and fast-changing area such as with COVID-19.

Indexed as

Coronavirus InfectionsPandemicsPneumonia, ViralPractice Guidelines as TopicChinaCOVID-19Decision Support Systems, ClinicalElectronic Health RecordsHumansarchetypeclinical guidelinescoronavirus diseaseCOVID-19knowledge modelingopenEHRtemplate

Identifiers

PMID32496207
PMCPMC7288685
OpenAlexW3030540375

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.