title: Archetype relational mapping - a practical openEHR persistence solution.
abstract: 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.
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University, Room 512, Zhouyiqing Building, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang, China.
authors.forename: Li
authors.initials: L
authors.lastname: Wang
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University, Room 512, Zhouyiqing Building, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang, China.
authors.forename: Lingtong
authors.initials: L
authors.lastname: Min
authors.affiliation: Department of Information Technology, Shanxi Dayi Hospital, Taiyuan, Shanxi, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Wang
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University, Room 512, Zhouyiqing Building, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang, China. lvxd@zju.edu.cn.
authors.forename: Xudong
authors.initials: X
authors.lastname: Lu
authors.affiliation: College of Biomedical Engineering and Instrument Science, Zhejiang University, Room 512, Zhouyiqing Building, Zhejiang University, 38 Zheda Road, Hangzhou, Zhejiang, China.
authors.forename: Huilong
authors.initials: H
authors.lastname: Duan
mesh_terms: D019992:Databases as Topic; D003695:Delivery of Health Care; D057286:Electronic Health Records; D006801:Humans; D008490:Medical Informatics