A structural model for quality requirements regarding Electronic Health Records - State of the art and first concepts

Summary Objectives: Since the first concepts for electronic health records (EHRs) in the 1990s, the content, structure, and technology of such records were frequently changed and adapted. The basic idea to support and enhance health care stayed the same over time. To reach these goals, it is crucial that EHRs themselves adhere to rigid quality requirements. The present review aims at describing the currently available, mainly non-functional, quality requirements with regard to electronic health records. Methods: A combined approach – systematic literature analysis and expert interviews – was used. The literature analysis as well as the expert interviews included sources /experts from different domains such as standards and norms, scientific literature and guidelines, and best practice. The expert interviews were performed by using problem-centric qualitative computer-assisted telephone interviews Methods Inf Med 2010; 49: 320–336 doi: 10.3414/ME10-01-0038 received: May 17, 2010 accepted: June 9, 2010 prepublished: July 6, 2010 (CATIs) or face-to-face interviews. All of the data that was obtained was analyzed using qualitative content analysis techniques. Results: In total, more than 1200 requirements were identified of which 203 requirements were also mentioned during the expert interviews. The requirements are organized according to the ISO 9126 and the eEurope 2002 criteria. Categories with the highest number of requirements found include global requirements, (general) functional requirements and data security. The number of nonfunctional requirements found is by contrast lower. Conclusion: The manuscript gives comprehensive insight into the currently available, primarily non-functional, EHR requirements. To our knowledge, there are no other publications that have holistically reported on this topic. The requirements identified can be used in different ways, e.g. the conceptual design, the development of EHR systems, as a starting point for further refinement or as a basis for the development of specific sets of requirements.

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

BMC Medical Informatics and Decision Making ◽

10.1186/s12911-021-01481-2 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Qi Tian ◽

Zhexi Han ◽

Ping Yu ◽

Jiye An ◽

Xudong Lu ◽

...

Keyword(s):

Electronic Health Records ◽

Data Quality ◽

Query Language ◽

Clinical Decision ◽

Data Repository ◽

Health Records ◽

Quality Requirements ◽

Electronic Health ◽

Data Tables

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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