Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2016 Sep;23(5):956-67.
doi: 10.1093/jamia/ocv137. Epub 2016 Jan 23.

An Exploratory Study Using an openEHR 2-level Modeling Approach to Represent Common Data Elements

Affiliations
Free PMC article

An Exploratory Study Using an openEHR 2-level Modeling Approach to Represent Common Data Elements

Ching-Heng Lin et al. J Am Med Inform Assoc. .
Free PMC article

Abstract

Background and objective: In order to facilitate clinical research across multiple institutions, data harmonization is a critical requirement. Common data elements (CDEs) collect data uniformly, allowing data interoperability between research studies. However, structural limitations have hindered the application of CDEs. An advanced modeling structure is needed to rectify such limitations. The openEHR 2-level modeling approach has been widely implemented in the medical informatics domain. The aim of our study is to explore the feasibility of applying an openEHR approach to model the CDE concept.

Materials and methods: Using the National Institute of Neurological Disorders and Stroke General CDEs as material, we developed a semiautomatic mapping tool to assist domain experts mapping CDEs to existing openEHR archetypes in order to evaluate their coverage and to allow further analysis. In addition, we modeled a set of CDEs using the openEHR approach to evaluate the ability of archetypes to structurally represent any type of CDE content.

Results: Among 184 CDEs, 28% (51) of the archetypes could be directly used to represent CDEs, while 53% (98) of the archetypes required further development (extension or specialization). A comprehensive comparison between CDEs and openEHR archetypes was conducted based on the lessons learnt from the practical modeling.

Discussion: CDEs and archetypes have dissimilar modeling approaches, but the data structure of both models are essentially similar. This study proposes to develop a comprehensive structure to model CDE concepts instead of improving the structure of CED.

Conclusion: The findings from this research show that the openEHR archetype has structural coverage for the CDEs, namely the openEHR archetype is able to represent the CDEs and meet the functional expectations of the CDEs. This work can be used as a reference when improving CDE structure using an advanced modeling approach.

Keywords: common data element; modeling approach; openEHR archetype.

Figures

Figure 1:
Figure 1:
(A) The UML class diagram of CDE content. Some attributes have been edited by shortening the content details. (B) An overview of the openEHR 2-level modeling approach.UML, Unified Modeling Language; CDE, common data element; ID, identification; EHR, electronic health record.
Figure 2:
Figure 2:
The Blood pressure concept represented by both CDE and archetype approachCDE, common data element.
Figure 3:
Figure 3:
An overview of the semiautomatic mapping workflowEHR, electronic health record; CDE, common data element.
Figure 4:
Figure 4:
(A) A stacked column chart of the search results. The percentage of each bar indicates the coverage ratio as defined in Equation 1 . In the table: AE, Assessments and Examinations; OEP, Outcomes and End Points; PSC, Participant/Subject Characteristics; PSHFH, Participant/Subject History and Family History; PE, Protocol Experience; SD, Safety Data; TID, Treatment/Intervention Data. (B) A pie chart of the existing archetype coverage in relation to all National Institute of Neurological Disorders and Stroke General common data elements.
Figure 5:
Figure 5:
A mind map of the Protocol Experience common data archetype model
Figure 6:
Figure 6:
A protocol experience form obtained via the openEHR Template DesignerEHR, electronic health record.

Similar articles

See all similar articles

Cited by 1 article

Publication types

Feedback