![]() An archetype should represent the maximal data set of a domain concept. Meanwhile, each archetype is designed towards reuse in other words, it should be agreed and shared to contribute to semantic interoperability among different EHR systems. Archetypes are the formal and semantic artifacts that facilitate collecting, storing, retrieving, representing, communicating and analyzing clinical data, which can be modeled by clinical professionals and health informatics experts by constraining RM. ![]() The AM consists of archetypes and templates. ![]() The RM is a stable and formal information model that focuses on the logical structures of an EHR and defines the basic structures and attributes needed to express EHR data instances, including data types, data structures, and components of an EHR. The openEHR approach has three major pillars: RM, AM, and terminology. It is a promising approach to facilitate the interoperation of EHR systems, which is based on the fact that a complete EHR dataset can be fully represented using sharable archetypes. The openEHR approach is a multi-level single source modeling within a service-oriented software framework. It enables the semantic interoperability and data sharing of EHRs, which differentiates the representation of data instances from the domain knowledge. The openEHR divides models into two levels (two-level modeling): the archetype model (AM) and the reference model (RM). OpenEHR is an open standard maintained by the openEHR Foundation, which endeavors to convert health data from a physical form into an electronic form and ensures universal interoperability among electronic data in all forms. This case study verified the feasibility of modeling EHR with the openEHR approach and identified the fact that the challenges such as localization, tool support, and an agile publishing process still exist for a broader application of the openEHR approach. The existing archetypes in CKM can faithfully represent most of the EHR requirements in China except customizations for local hospital management. ![]() The legacy data of the EHR system in hospitals could be integrated into the CDR developed with these archetypes successfully. Meanwhile, 6 (9%) archetypes were newly developed. 59 (91%) archetypes could be found in Clinical Knowledge Manager (CKM), of which 35 could be reused directly without change, and 23 required further development including two revisions, two new versions, 18 extensions and one specialization. Sixty four archetypes were developed to represent all requirements of a complete EHR dataset. Based on the models developed in this case study, we have implemented a clinical data repository (CDR) to verify the feasibility of modeling EHR with archetypes. Two representative EHR systems from Chinese vendors and the existing Chinese EHR standards have been used as resources to identify the requirements of EHR in China, and a case study of modeling EHR in China has been conducted. We proposed an archetype modeling method including an iterative process of collecting requirements, normalizing data elements, organizing concepts, searching corresponding archetypes, editing archetypes and reviewing archetypes. This paper presents a case study of modeling an EHR in China aiming to investigate the feasibility and challenges of archetyping a complete EHR dataset with the openEHR approach. Although the openEHR approach has been applied in different domains, the feasibility of archetyping a complete EHR dataset in a hospital has not been reported in academic literature, especially in a country where using openEHR is still in its infancy stage, like China. Developing archetypes for the complete EHR dataset is essential for implementing a large-scale interoperable EHR system with the openEHR approach. The openEHR approach can improve the interoperability of electronic health record (EHR) through two-level modeling.
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