@article{ElgertRichterKatzensteineretal.2023,
  author    = {Elgert, Lena and Richter, Jendrik and Katzensteiner, Matthias and Joseph, Mareike and Hellmers, Sandra and Bott, Oliver J. and Wolf, Klaus-Hendrik},
  title     = {Towards a Recommendation for Good Health Data Modeling (GHDM) - Results of Expert Interviews},
  journal   = {German Medical Data Sciences 2023 - Science. Close to People (Studies in Health Technology and Informatics ; 307)},
  doi       = {10.25968/opus-2953},
  institution = {Fakult{\"a}t III - Medien, Information und Design},
  pages     = {215 -- 221},
  year      = {2023},
  abstract  = {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.},
  subject      = {Gesundheitsinformationssystem},
  language  = {en}
}