Structuring Healthcare Data Models: Recommendations for Standardization and Multi-faceted Approaches
Empfehlungen zur Datenmodellierung im Gesundheitswesen - E-HEALTH-COM
Summary
Researchers emphasize that systematic data modeling is crucial for efficient, future-proof healthcare information sharing. They recommend that while HL7 FHIR excels in data exchange and interoperability, the appropriate standard must be chosen based on specific goals, such as analysis or organizational structure.
Details
Health data is immensely valuable for medical treatment and research, but its potential requires uniform structuring and cross-site exchange. A data model provides a structured description of data attributes and relationships, enabling consistent storage, processing, and exchange. This process involves multiple levels: defining the domain (functional level), describing the formal structure (logical level), and implementing the concrete technology (technical level). Several international standards can be used for modeling, and selection depends on the use case. HL7 FHIR is a key standard focusing on data exchange and interoperability between different IT systems. Other prominent standards include OMOP (focused on cross-institutional analysis) and openEHR (emphasizing model reusability). The research team concluded that no single 'correct' standard exists; the optimal choice varies depending on the intended purpose and use case. Furthermore, selecting a data model requires considering not only technical aspects but also human factors (user goals), information technological factors (reusability across projects), and organizational governance structures. This study aims to provide practical guidelines for selecting and applying healthcare data models based on these findings.
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