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Archetype relational mapping - a practical openEHR persistence solution

机译：原型关系映射-实用的openEHR持久性解决方案

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Background One of the primary obstacles to the widespread adoption of openEHR methodology is the lack of practical persistence solutions for future-proof electronic health record (EHR) systems as described by the openEHR specifications. This paper presents an archetype relational mapping (ARM) persistence solution for the archetype-based EHR systems to support healthcare delivery in the clinical environment. Methods First, the data requirements of the EHR systems are analysed and organized into archetype-friendly concepts. The Clinical Knowledge Manager (CKM) is queried for matching archetypes; when necessary, new archetypes are developed to reflect concepts that are not encompassed by existing archetypes. Next, a template is designed for each archetype to apply constraints related to the local EHR context. Finally, a set of rules is designed to map the archetypes to data tables and provide data persistence based on the relational database. Results A comparison study was conducted to investigate the differences among the conventional database of an EHR system from a tertiary Class A hospital in China, the generated ARM database, and the Node?+?Path database. Five data-retrieving tests were designed based on clinical workflow to retrieve exams and laboratory tests. Additionally, two patient-searching tests were designed to identify patients who satisfy certain criteria. The ARM database achieved better performance than the conventional database in three of the five data-retrieving tests, but was less efficient in the remaining two tests. The time difference of query executions conducted by the ARM database and the conventional database is less than 130?%. The ARM database was approximately 6–50 times more efficient than the conventional database in the patient-searching tests, while the Node?+?Path database requires far more time than the other two databases to execute both the data-retrieving and the patient-searching tests. Conclusions The ARM approach is capable of generating relational databases using archetypes and templates for archetype-based EHR systems, thus successfully adapting to changes in data requirements. ARM performance is similar to that of conventionally-designed EHR systems, and can be applied in a practical clinical environment. System components such as ARM can greatly facilitate the adoption of openEHR architecture within EHR systems.
机译：背景技术广泛采用openEHR方法的主要障碍之一是，缺乏针对openEHR规范所描述的面向未来的电子健康记录（EHR）系统的实用持久性解决方案。本文为基于原型的EHR系统提供了原型关系映射（ARM）持久性解决方案，以支持临床环境中的医疗保健提供。方法首先，对EHR系统的数据需求进行分析并将其组织为原型友好的概念。向临床知识经理（CKM）查询匹配的原型;必要时，将开发新的原型以反映现有原型未涵盖的概念。接下来，为每个原型设计一个模板，以应用与本地EHR上下文有关的约束。最后，设计了一组规则以将原型映射到数据表并基于关系数据库提供数据持久性。结果进行了一项比较研究，以调查中国一家三级甲等医院的EHR系统的常规数据库，生成的ARM数据库和Node？+？Path数据库之间的差异。根据临床工作流程设计了五种数据检索测试，以检索检查和实验室测试。此外，还设计了两个患者搜索测试来识别满足某些标准的患者。在五个数据检索测试中的三个中，ARM数据库的性能比常规数据库更好，但在其余两个测试中效率较低。 ARM数据库和常规数据库执行查询的时间差小于130％。在患者搜索测试中，ARM数据库的效率比传统数据库高出大约6–50倍，而Node？+？Path数据库需要比其他两个数据库更多的时间来执行数据检索和患者数据查询。搜索测试。结论ARM的方法能够为基于原型的EHR系统使用原型和模板生成关系数据库，从而成功地适应了数据需求的变化。 ARM的性能类似于常规设计的EHR系统，并且可以在实际的临床环境中使用。系统组件（例如ARM）可以极大地促进在EHR系统中采用openEHR体系结构。

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