Ching-Heng Lin1, Yang-Cheng Fann2, Der-Ming Liou3. 1. Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan. 2. National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, Maryland, USA. 3. Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan dmliou@ym.edu.tw.
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.
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.
Authors: Petra Knaup; Sebastian Garde; Angela Merzweiler; Norbert Graf; Freimut Schilling; Ralf Weber; Reinhold Haux Journal: Int J Med Inform Date: 2005-08-22 Impact factor: 4.046
Authors: Alfred E Buxton; Hugh Calkins; David J Callans; John P DiMarco; John D Fisher; H Leon Greene; David E Haines; David L Hayes; Paul A Heidenreich; John M Miller; Athena Poppas; Eric N Prystowsky; Mark H Schoenfeld; Peter J Zimetbaum; Paul A Heidenreich; David C Goff; Frederick L Grover; David J Malenka; Eric D Peterson; Martha J Radford; Rita F Redberg Journal: J Am Coll Cardiol Date: 2006-12-05 Impact factor: 24.094