Archetype relational mapping - a practical openEHR persistence solution

BMC Medical Informatics and Decision Making

Research Article

Xudong Lu¹  Huilong Duan¹  Li Wang¹  Lingtong Min¹  Rui Wang² 

[1] College of Biomedical Engineering and Instrument Science, Zhejiang University, Room 512, Zhouyiqing Building, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang, China;Department of Information Technology, Shanxi Dayi Hospital, Taiyuan, Shanxi, China;

关键词: Archetype relational mapping;    Archetype;    OpenEHR;    Relational database;    Data persistence;   

DOI  :  10.1186/s12911-015-0212-0

 received in 2015-03-20, accepted in 2015-10-19,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundOne 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.MethodsFirst, 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.ResultsA 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© Wang et al. 2015