A FRAMEWORK FOR ENSURING DATA INTEGRITY IN HEALTHCARE DATA MIGRATION
DOI:
https://doi.org/10.53555/cse.v11i1.2474Keywords:
Data Migration, Data Integrity, JSON, SQLAbstract
The digital transformation of healthcare systems has accelerated the adoption of electronic health records (EHRs) and data-driven technologies. As healthcare institu- tions transition from legacy systems to modern platforms, data migration has become a critical process for consolidating, storing, and analyzing patient data. Despite its im- portance, data migration introduces significant risks to data integrity. Missing records, corrupted values, duplication, and schema mismatches during migration can lead to inaccurate or incomplete datasets. In the healthcare domain, such inconsistencies can have severe consequences, including misdiagnosis, delayed treatments, financial errors, and regulatory non-compliance. Therefore, maintaining strict data integrity during migration is essential to ensuring patient safety, trustworthiness of clinical decisions, and adherence to standards such as HIPAA, GDPR, and HL7/FHIR.
Healthcare data is highly complex, often containing patient demographics, diag- nostic information, laboratory results, and treatment histories. The heterogeneity of formats—ranging from spreadsheets and CSV files to JSON structures and relational SQL databases—compounds the challenge of reliable migration. Traditional migration tools primarily focus on transferring data efficiently but lack comprehensive mechanisms for validating integrity at every stage. This gap necessitates a structured framework that integrates validation techniques to guarantee that data remains accurate, complete, and consistent after migration.
This study addresses these challenges by proposing a framework for healthcare data migration that emphasizes integrity validation. The methodology involves simulating the migration of an open healthcare dataset from flat file formats (CSV/Excel) into JSON and SQL databases. Pre- and post-migration validation is performed using au- tomated methods such as record count comparison, duplicate detection, and checksum verification. Additionally, domain-specific data quality rules are applied to verify the accuracy of mandatory attributes such as patient ID, age, and diagnosis. Python li- braries, including pandas for data manipulation and hashlib for checksum computation, are employed to design a lightweight validation pipeline. The framework aims to provide a reproducible and efficient approach to healthcare data migration, with the ultimate goal of minimizing risks and ensuring data reliability in clinical environments.
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