gms | German Medical Science

62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS)

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie

17.09. - 21.09.2017, Oldenburg

Acquiring routinely collected claims data from multiple European health insurances for dental research: Lessons learned

Meeting Abstract

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  • Christian Haux - Universität Heidelberg, Heidelberg, Deutschland
  • Kasper Rosing - University of Copenhagen, Kopenhagen, Dänemark
  • Olivier Kalmus - Universitätsklinikum Heidelberg, Heidelberg, Deutschland
  • Petra Knaup - Universität Heidelberg, Heidelberg, Deutschland
  • Stefan Listl - Universitätsklinikum Heidelberg, Heidelberg, Deutschland

Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie. 62. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e.V. (GMDS). Oldenburg, 17.-21.09.2017. Düsseldorf: German Medical Science GMS Publishing House; 2017. DocAbstr. 283

doi: 10.3205/17gmds167, urn:nbn:de:0183-17gmds1678

Veröffentlicht: 29. August 2017

© 2017 Haux 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: Using claims data for research has great potential [1]. Access to data is often challenging, influenced by complex privacy regulations [2]. We share our experiences during acquisition of claims data in different nations of the EU-funded project ADVOCATE (Added Value for Oral Care). ADVOCATE aims to identify strategies to prevent oral diseases by analyzing data from health insurances, health funds and health authorities from Denmark, Germany, Hungary, Ireland, the Netherlands and the United Kingdom [3].

State of the art: Data are stored in the databases of their owners and specific data sets were generated for ADVOCATE. Since the data contain identifying information, strict data protection and privacy measures, following local legal regulations have to be established [4]. The variety of these regulations requires different approaches for data acquisition. E.g., some data owners demand aggregation of data, whereas others allow usage of individual-level data. Therefore, a complex approach for designing data acquisition processes is necessary.

Concept: We designed these processes for ADVOCATE, all of them following the guidelines for “Good Practice of Secondary Data Analysis” [5]. First, we consented an individual data use agreement (DUA) with each data owner. In order to determine specific activities and implementation of the processes, we performed a qualitative content analysis on the DUAs [6], [7].

Implementation: Data acquisition during ADVOCATE was influenced by both lack of experience of data owners in sharing their data for research and the absence of data usage rules. Therefore, we set up a pragmatic process: After determining the prerequisites, all data owners shared a data excerpt to check if all regulations were adhered to. After that, we evaluated the quality of the data, following the recommendations of Horenkamp-Sonntag et al. [8]. When the quality of the excerpt was suitable for analyses within the ADVOCATE project, eight data owners delivered the full-scale data sets, some of them containing millions of records.

Lessons Learned: Determining the requirements and regulations for data usage differed among data owners. All but one data owner demanded to consent a DUA. The majority of the data owners provided a template for the DUA. For those, who did not, we designed a new one. Specific regulations in the DUAs influenced the data transfer and storage approaches. Most data owners allowed data transfer via the Internet, except for the German data owner. In this case, we developed data analysis routines using the data excerpt, executed them on the data in the premises of the data owner and exported aggregated results. If data were allowed to be transferred via Internet, we had to implement different security measures and set up multiple systems to adhere to all privacy regulations. Also, data contents and qualities differed due to the underlying heterogeneous regulations.

Our experiences showed, that accessing and using claims data for research is challenging. Nevertheless, we are now able to analyze data for care research on a unique and extensive international dataset. Our approach can be refined towards a standard operating procedure to provide researchers with a methodological framework.

Acknowledgements: This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 635183.

Die Autoren geben an, dass kein Interessenkonflikt besteht.

Die Autoren geben an, dass ein positives Ethikvotum vorliegt.


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