Digitalizing MoCHiV Data | #sciencefather #scientistaward #database #REDCap #DataMigration

Digitizing Longitudinal Cohort Data: Migration of the MoCHiV Database from Oracle SQL to REDCap 

Introduction 

The global shift toward digital health has brought about significant changes in how public health data is collected, stored, and managed. In clinical research, particularly in longitudinal studies involving complex patient populations, digital solutions are increasingly essential. The Swiss Mother and Child HIV Cohort Study (MoCHiV) serves as a powerful example of this transformation. Established in 2003, MoCHiV integrates pediatric and maternal data related to HIV from various centers across Switzerland. Over the years, it has generated a vast dataset, previously managed through manual paper-based processes and stored in an Oracle SQL database. As the need for more efficient, accessible, and interoperable systems grew, the migration to Research Electronic Data Capture (REDCap) became a logical next step in the study's evolution.


Database Structure and Migration Strategy

Migrating from Oracle SQL to REDCap was a complex, multi-step process involving significant planning and customization. The Oracle system featured a traditional relational database with interconnected tables representing pregnancies, deliveries, treatments, lab results, and follow-up visits. This structure had to be reimagined to fit REDCap’s form-based, longitudinal project model. Three distinct study arms were designed in REDCap to reflect the relationships between mothers, pregnancies, and children. These arms allowed for flexible data entry based on whether a child was born into the study, adopted later, or enrolled independently.

Data Cleaning and Standardization

One of the most technically demanding steps in the migration process was cleaning and reformatting the legacy data to match the structure required by REDCap. This involved merging primary keys, standardizing categorical variables, and reconciling inconsistencies in dates and identifiers. For instance, different tables in the Oracle system used different keys for pregnancies, deliveries, and newborns, requiring the development of R algorithms to link and validate entries across tables. Data on treatments and lab values had to be merged and reformatted into the long format expected by REDCap, with careful attention to preserving metadata such as physician notes and comments.

Ensuring Data Accuracy and Compliance

After the initial data migration, rigorous testing was conducted to ensure the accuracy and completeness of the transferred data. REDCap reports were exported and compared systematically with Oracle records using custom R scripts. Inconsistencies were flagged, investigated, and resolved, sometimes involving manual review of archived paper forms. Moreover, to maintain ethical standards and patient confidentiality, REDCap's Data Access Group functionality was used to control user access based on site affiliation, ensuring that users could only view data relevant to their own center.

Integration and Interoperability

A key goal of the project was to promote interoperability between MoCHiV and related systems, such as the Swiss HIV Cohort Study (SHCS). While REDCap is highly capable of handling new data entry, integration with existing platforms required additional customization. Docker containers were used to deploy REDCap, Oracle, and Django-based applications on a unified server infrastructure with automated backups, ensuring data security and scalability. This setup also supports future interoperability efforts, such as exporting data to clinical data warehouses or integrating with electronic health record systems.

Impact and Future Directions

The successful migration of MoCHiV to REDCap represents a major milestone in the digital modernization of HIV research in Switzerland. It not only enhances data accessibility and quality but also lays the groundwork for broader digital health initiatives. The modular nature of REDCap means that future features—such as mobile data collection, real-time analytics, and automated reporting—can be added as needed. Moreover, by using open standards and transparent methodologies, the MoCHiV team has created a framework that other cohort studies can follow, especially those operating in complex, multi-center environments.

#REDCap #DataMigration #DigitalHealth #MoCHiV #ClinicalDataManagement #HealthInformatics #HIVResearch #PublicHealthData #OracleToREDCap #CohortStudy #MedicalData #DataInteroperability #DigitalTransformation #HealthcareIT #ResearchDataManagement

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