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Towards a Recommendation for Good Health Data Modeling (GHDM) – Results of Expert Interviews

  • Appropriate data models are essential for the systematic collection, aggregation, and integration of health data and for subsequent analysis. However, recommendations for modeling health data are often not publicly available within specific projects. Therefore, the project Zukunftslabor Gesundheit investigates recommendations for modeling. Expert interviews with five experts were conducted and analyzed using qualitative content analysis. Based on the condensed categories “governance”, “modeling” and “standards”, the project team generated eight hypotheses for recommendations on health data modeling. In addition, relevant framework conditions such as different roles, international cooperation, education/training and political influence were identified. Although emerging from interviewing a small convenience sample of experts, the results help to plan more extensive data collections and to create recommendations for health data modeling.

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Author:Lena Elgert, Jendrik Richter, Matthias KatzensteinerORCiD, Mareike Joseph, Sandra Hellmers, Oliver J. BottORCiDGND, Klaus-Hendrik Wolf
DOI original:https://doi.org/10.3233/SHTI230716
Parent Title (English):German Medical Data Sciences 2023 – Science. Close to People (Studies in Health Technology and Informatics ; 307)
Document Type:Article
Year of Completion:2023
Publishing Institution:Hochschule Hannover
Release Date:2023/09/21
Tag:data model; expert interview; health data; health information systems
GND Keyword:Gesundheitsinformationssystem; Datenmodell; Experteninterview
First Page:215
Last Page:221
Institutes:Fakultät III - Medien, Information und Design
DDC classes:610 Medizin, Gesundheit
Licence (German):License LogoCreative Commons - CC BY-NC - Namensnennung - Nicht kommerziell 4.0 International