Literature DB >> 29454107

Archetype modeling methodology.

David Moner1, José Alberto Maldonado2, Montserrat Robles3.   

Abstract

Clinical Information Models (CIMs) expressed as archetypes play an essential role in the design and development of current Electronic Health Record (EHR) information structures. Although there exist many experiences about using archetypes in the literature, a comprehensive and formal methodology for archetype modeling does not exist. Having a modeling methodology is essential to develop quality archetypes, in order to guide the development of EHR systems and to allow the semantic interoperability of health data. In this work, an archetype modeling methodology is proposed. This paper describes its phases, the inputs and outputs of each phase, and the involved participants and tools. It also includes the description of the possible strategies to organize the modeling process. The proposed methodology is inspired by existing best practices of CIMs, software and ontology development. The methodology has been applied and evaluated in regional and national EHR projects. The application of the methodology provided useful feedback and improvements, and confirmed its advantages. The conclusion of this work is that having a formal methodology for archetype development facilitates the definition and adoption of interoperable archetypes, improves their quality, and facilitates their reuse among different information systems and EHR projects. Moreover, the proposed methodology can be also a reference for CIMs development using any other formalism.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Archetype; Dual model; ISO 13606; Methodology; Openehr

Mesh:

Year:  2018        PMID: 29454107     DOI: 10.1016/j.jbi.2018.02.003

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  4 in total

1.  A refined methodology for validation of information models derived from flowsheet data and applied to a genitourinary case.

Authors:  Bonnie L Westra; Kay S Lytle; Luann Whittenburg; Mischa Adams; Samira Ali; Meg Furukawa; Stephanie Hartleben; Mary Hook; Steve Johnson; Sarah Collins Rossetti; Tess Theresa Settergren
Journal:  J Am Med Inform Assoc       Date:  2020-11-01       Impact factor: 4.497

2.  Discovering Clinical Information Models Online to Promote Interoperability of Electronic Health Records: A Feasibility Study of OpenEHR.

Authors:  Lin Yang; Xiaoshuo Huang; Jiao Li
Journal:  J Med Internet Res       Date:  2019-05-28       Impact factor: 5.428

3.  Defining health data elements under the HL7 development framework for metadata management.

Authors:  Zhe Yang; Kun Jiang; Miaomiao Lou; Yang Gong; Lili Zhang; Jing Liu; Xinyu Bao; Danhong Liu; Peng Yang
Journal:  J Biomed Semantics       Date:  2022-03-18

4.  Nutrition Information in Oncology - Extending the Electronic Patient-Record Data Set.

Authors:  Priscila A Maranhão; Ana Margarida Pereira; Conceição Calhau; Paula Ravasco; Federico Bozzetti; Alessandro Laviano; Liz Isenring; Elisa V Bandera; Maureen B Huhmann; Pedro Vieira-Marques; Ricardo J Cruz-Correia
Journal:  J Med Syst       Date:  2020-09-28       Impact factor: 4.460

  4 in total

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