TY - JOUR U1 - Wissenschaftlicher Artikel A1 - Elgert, Lena A1 - Richter, Jendrik A1 - Katzensteiner, Matthias A1 - Joseph, Mareike A1 - Hellmers, Sandra A1 - Bott, Oliver J. A1 - Wolf, Klaus-Hendrik T1 - Towards a Recommendation for Good Health Data Modeling (GHDM) – Results of Expert Interviews JF - German Medical Data Sciences 2023 – Science. Close to People (Studies in Health Technology and Informatics ; 307) N2 - 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. KW - health information systems KW - health data KW - data model KW - expert interview KW - Gesundheitsinformationssystem KW - Datenmodell KW - Experteninterview Y1 - 2023 UN - https://nbn-resolving.org/urn:nbn:de:bsz:960-opus4-29531 U6 - https://doi.org/10.25968/opus-2953 DO - https://doi.org/10.25968/opus-2953 SP - 215 EP - 221 ER -