<![CDATA[Hybrid healthcare ecosystems increasingly integrate traditional healthcare infrastructures with cloud-based platforms to support operational transactions, advanced analytics, scalability, and regulatory requirements. However, the coexistence of these heterogeneous infrastructures creates significant challenges related to data representation, synchronization, consistency, traceability, and governance. Traditional infrastructures remain essential for maintaining transactional consistency in critical healthcare processes, including billing, eligibility verification, enrollment, claims processing, and provider management. Meanwhile, cloud platforms provide flexible computing resources, distributed data processing, and enhanced analytical capabilities. Differences in database schemas, data formats, ingestion mechanisms, processing architectures, and transformation procedures can consequently introduce discrepancies and reduce confidence in cross-system healthcare data. This study proposes a validated cross-system data structure designed to improve the reliability and integrity of data synchronization within hybrid healthcare ecosystems. The proposed approach incorporates four primary validation components: source validation, transformation validation, inter-system reconciliation, and lineage monitoring. These components are complemented by additional validation mechanisms, including schema validation, record-level validation, time-window validation, and hash-based validation, to detect inconsistencies at different stages of the data lifecycle. Source validation verifies the completeness, accuracy, and conformity of incoming data, while transformation validation evaluates whether data-processing rules preserve semantic and structural consistency. Inter-system reconciliation compares records across traditional and cloud environments to identify missing, duplicated, or inconsistent transactions. Lineage monitoring provides traceability by documenting data origins, transformations, dependencies, and destinations throughout the processing pipeline. Furthermore, automation and orchestration mechanisms are incorporated into extract-transform-load (ETL) workflows and cloud computing environments to enable continuous validation, reduce manual intervention, and improve scalability. The proposed structure provides a systematic framework for strengthening data integrity across heterogeneous healthcare infrastructures. Its implementation can improve synchronization reliability, enhance auditability, support compliance readiness, and reduce the risks associated with inconsistent healthcare information. The framework also enables healthcare organizations to maintain dependable data flows while progressively adopting cloud technologies without compromising the transactional reliability of legacy systems. Overall, the study demonstrates that integrating automated validation, reconciliation, and lineage monitoring into hybrid data architectures can contribute to more reliable, transparent, scalable, and governance-oriented healthcare information systems capable of supporting both operational decision-making and evolving regulatory demands]]>
