gms | German Medical Science

66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e. V. (TMF)

26. - 30.09.2021, online

Reporting on Data Quality Approaches in Health Information Systems

Meeting Abstract

  • Lucas Triefenbach - Medizinische Fakultät der RWTH Aachen, Aachen, Germany
  • Jonas Bienzeisler - Medizinische Fakultät der RWTH Aachen, Aachen, Germany
  • Atinkut Alamirrew Zeleke - Universität Greifswald, Greifswald, Germany
  • Alexander Kombeiz - Medizinische Fakultät der RWTH Aachen, Aachen, Germany
  • Ronny Otto - Universitätsklinikum Magdeburg A.ö.R., Magdeburg, Germany
  • Raphael W. Majeed - Medizinische Fakultät der RWTH Aachen, Aachen, Germany
  • Rainer Röhrig - Medizinische Fakultät der RWTH Aachen, Aachen, Germany

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 66. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS), 12. Jahreskongress der Technologie- und Methodenplattform für die vernetzte medizinische Forschung e.V. (TMF). sine loco [digital], 26.-30.09.2021. Düsseldorf: German Medical Science GMS Publishing House; 2021. DocAbstr. 25

doi: 10.3205/21gmds066, urn:nbn:de:0183-21gmds0665

Veröffentlicht: 24. September 2021

© 2021 Triefenbach et al.
Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). Lizenz-Angaben siehe



Introduction: In healthcare research, the aggregation and processing of medical data for secondary use in health information systems such as registries or electronic health records is increasing [1]. Errors in data acquisition affect primary data level users [2], but also secondary users such as decisions support systems and research [3], [4]. In recent years, several reviews of data quality in healthcare have explored how data quality can be measured and is being measured [5], [6], [7], [8], [9]. While these works indicate a wealth of literature concerning the development and assessment of data quality indices, there seems to be a gap in literature with regard to how data quality issues are approached.

Methods: We conducted a preliminary scoping review using the Joanna Briggs Institute methodology [10]. Our approach consisted of a literature search using a Medline search limited to the works from 01.01.2018 till 25.03.2021 and including terms related to data quality and electronic health/medical records.

Inspired by the risk management for technology induced errors [11] we developed a classification to capture the procedural nature of different works. Fundamentally, a data quality management process consists of three basic components: Data Control (DC), the process of improving data quality by interventions, Data Quality Assessment (DQA), the process of quantifying and evaluating data quality, and Continuous Data Quality Monitoring (Monitoring), the process to control monitoring of data quality. The works were classified based on these categories by 2 team members independently, and a third team member was used to resolve conflicts.

Results: The searches in Medline and inclusion of manually searched literature resulted in 591 records to be screened by titles and abstracts. Out of these, 126 records were chosen for full-text assessment, with 20 being excluded for being inaccessible or out of scope. Finally, 104 records were classified using the previously presented classification. Out of 104 records 53 were describing DQA, 26 DC, 1 Monitoring, 2 DQA & Monitoring, 9 DQA & DC and 1 DC & Monitoring. 10 works included the complete process consisting of all 3 processes.

Discussion and Conclusion: As hypothesized, the main focus of current literature is on DQA methodology, while the second most popular type of works is DC on its own. Monitoring is not discussed in isolation, which seems to be a logical consequence of monitoring being dependent on DQA. Within its time limitation, our approach identifies a bias towards high level approaches in data quality reporting. Most of the current approaches focus on one to two aspects of the data quality management process. While raising awareness of data quality issues seems to be the current scope of literature, more evidence for the functioning of generic data quality methodology seems indicated. The current version of the scoping review is limited to the PubMed database search and the analysis of the theme to the main classification levels. We anticipate addressing those issues in the upcoming extended versions of this work and to publish them in a peer reviewed journal.

Supplemental Material: The classification is available from:

The authors declare that they have no competing interests.

The authors declare that an ethics committee vote is not required.


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