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

Area of Science:

• Health Informatics
• Data Science
• Public Health

Background:

• Effective data models are vital for health data collection, aggregation, integration, and analysis.
• Lack of publicly available modeling recommendations hinders systematic health data management in projects.

Purpose of the Study:

• To investigate and formulate recommendations for health data modeling.
• To address the gap in accessible health data modeling guidelines.

Main Methods:

• Qualitative content analysis of expert interviews.
• Exploration of framework conditions influencing health data modeling.

Main Results:

• Eight hypotheses for health data modeling recommendations were generated.
• Key categories identified: governance, modeling, and standards.
• Framework conditions include roles, international cooperation, education, and political influence.

Conclusions:

• The findings provide a foundation for more extensive data collection planning.
• Results aid in creating practical recommendations for health data modeling.
• Highlights the importance of governance, standards, and collaboration in health data initiatives.