Generating openEHR Archetypes and Templates with Annotated Mindmap Models

The medical information model serves as a crucial foundation for effectively organizing and expressing medical information to ensure interoperability of information systems. The openEHR approach is increasingly recognized as a means to achieve interoperability among medical information systems. However, the creation of archetypes and templates can be complex and time-consuming, potentially impeding their adoption in healthcare settings.

This study aims to investigate and propose a method for the automatic generation of archetypes and templates within the openEHR architecture. The objective is to simplify and streamline the process of archetype and template creation.

We introduced an automated approach to generate openEHR archetypes and templates using a well-annotated mindmap. First, we defined a set of annotation specifications for annotating the business models. Second, we developed a method to analyze this mindmap, identifying potential archetypes and templates along with their contents, based on these annotations. The resulting archetypes and templates were created in compliance with openEHR standards.

We developed a prototype system capable of automatically generating openEHR archetypes and templates. The source code for this system is available on GitHub at https://github.com/cloudphr/mindehr. Our experimental results demonstrate that the system can efficiently parse node information from mindmaps and generate archetype and template files accurately. When compared to traditional modeling methods employed in practical work, our proposed method achieved a significant time saving.

The method we proposed reveals a new pattern for clinical information modeling. It can generate archetypes and templates that meet the openEHR specification and business requirements. Moreover, it offers a viable automated data modeling tool for clinical informatics researchers, thereby enhancing modeling efficiency.