Towards a Recommendation for Good Health Data Modeling (GHDM) - Results of Expert Interviews.
Stud Health Technol Inform
; 307: 215-221, 2023 Sep 12.
Article
en En
| MEDLINE
| ID: mdl-37697856
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.
Palabras clave
Texto completo:
1
Base de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Cooperación Internacional
Tipo de estudio:
Prognostic_studies
/
Qualitative_research
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2023
Tipo del documento:
Article