German statutory health insurance (SHI) claims data are a major external data source for health research, enabling population-wide real-world evidence (RWE) across sectors. Their full potential is particularly relevant in federated healthcare research infrastructures, where linkage with clinically generated data may support more comprehensive and longitudinal analyses. However, routine claims data are collected primarily for reimbursement rather than research, and their reuse in such infrastructures requires systematic assessment of data suitability, particularly through the assessment of data quality and standardized representability. This work therefore presents a methodological concept to (i) develop a claims-specific data quality assessment (DQA) approach by synthesizing data quality challenges reported across diverse claims-based settings in the literature and operationalizing them through a rule-based validation pipeline for incoming claims data and (ii) assess the structural representability of SHI data elements in standardized HL7 FHIR profiles for enhancing structural interoperability. Expected outcomes include a healthcare claims data-specific catalog of data quality issues, evidence-informed validations, a mapping coverage analysis, and the identification of representational gaps. This proposed concept provides a structural methodological basis for assessing German SHI claims data suitability and may support future harmonization, linkage, and secondary use of claims data.