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BMC Medical Informatics and Decision Making

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Open Access 01-12-2021 | Research article

Application of openEHR archetypes to automate data quality rules for electronic health records: a case study

Authors: Qi Tian, Zhexi Han, Ping Yu, Jiye An, Xudong Lu, Huilong Duan

Published in: BMC Medical Informatics and Decision Making | Issue 1/2021

Abstract

Background

Ensuring data is of appropriate quality is essential for the secondary use of electronic health records (EHRs) in research and clinical decision support. An effective method of data quality assessment (DQA) is automating data quality rules (DQRs) to replace the time-consuming, labor-intensive manual process of creating DQRs, which is difficult to guarantee standard and comparable DQA results. This paper presents a case study of automatically creating DQRs based on openEHR archetypes in a Chinese hospital to investigate the feasibility and challenges of automating DQA for EHR data.

Methods

The clinical data repository (CDR) of the Shanxi Dayi Hospital is an archetype-based relational database. Four steps are undertaken to automatically create DQRs in this CDR database. First, the keywords and features relevant to DQA of archetypes were identified via mapping them to a well-established DQA framework, Kahn’s DQA framework. Second, the templates of DQRs in correspondence with these identified keywords and features were created in the structured query language (SQL). Third, the quality constraints were retrieved from archetypes. Fourth, these quality constraints were automatically converted to DQRs according to the pre-designed templates and mapping relationships of archetypes and data tables. We utilized the archetypes of the CDR to automatically create DQRs to meet quality requirements of the Chinese Application-Level Ranking Standard for EHR Systems (CARSES) and evaluated their coverage by comparing with expert-created DQRs.

Results

We used 27 archetypes to automatically create 359 DQRs. 319 of them are in agreement with the expert-created DQRs, covering 84.97% (311/366) requirements of the CARSES. The auto-created DQRs had varying levels of coverage of the four quality domains mandated by the CARSES: 100% (45/45) of consistency, 98.11% (208/212) of completeness, 54.02% (57/87) of conformity, and 50% (11/22) of timeliness.

Conclusion

It’s feasible to create DQRs automatically based on openEHR archetypes. This study evaluated the coverage of the auto-created DQRs to a typical DQA task of Chinese hospitals, the CARSES. The challenges of automating DQR creation were identified, such as quality requirements based on semantic, and complex constraints of multiple elements. This research can enlighten the exploration of DQR auto-creation and contribute to the automatic DQA.

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Title: Application of openEHR archetypes to automate data quality rules for electronic health records: a case study
Authors: Qi Tian
Zhexi Han
Ping Yu
Jiye An
Xudong Lu
Huilong Duan
Publication date: 01-12-2021
Publisher: BioMed Central
Published in: BMC Medical Informatics and Decision Making / Issue 1/2021
Electronic ISSN: 1472-6947
DOI: https://doi.org/10.1186/s12911-021-01481-2

At a glance: The STEP trials

Springer Medicine

Application of openEHR archetypes to automate data quality rules for electronic health records: a case study

Abstract

Background

Methods

Results

Conclusion

At a glance: The STEP trials

Springer Medicine

Abstract

Background

Methods

Results

Conclusion

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