Identifying Relevant FHIR Elements for Data Quality Assessment in the German Core Data Set.

Area of Science:

• Medical Informatics
• Biomedical Research
• Health Data Standards

Background:

• The German Medical Informatics Initiative (MII) facilitates biomedical research using clinical routine data from 37 university hospitals.
• Data Integration Centers (DICs) are established to manage and enable data reuse across these institutions.
• A standardized HL7 FHIR (Health Level Seven Fast Healthcare Interoperability Resources) profiles, the "MII Core Data Set," ensures data consistency.

Purpose of the Study:

• To propose a process for identifying relevant elements within FHIR profiles for data quality assessment.
• To support data integration centers in establishing robust data quality measures for clinical research.
• To align data quality assessments with established frameworks, specifically Kahn et al.'s measures.

Main Methods:

• Developing a systematic process to extract specific data elements from HL7 FHIR profiles.
• Focusing on data quality measures as defined by Kahn et al.
• Applying the process within the context of the German Medical Informatics Initiative's data sharing framework.

Main Results:

• A defined process for selecting critical data elements from FHIR profiles for quality assessment.
• Demonstrated applicability of the process in supporting data quality initiatives within data integration centers.
• Foundation for enhancing trust and reliability in clinical research data.

Conclusions:

• The proposed process aids in establishing data quality assessments for FHIR-based clinical data sharing.
• Effective data quality assessment is crucial for trustworthy biomedical research.
• Standardized data models and quality checks are essential for large-scale health data initiatives.