This community innovation has been accepted at the 2026 DHIS2 Annual Conference as a physical poster.
Strengthening DHIS2 Data Quality Through Automated
High quality routine health data is essential for effective planning, monitoring, and decision making. DHIS2 provides a wide range of data quality mechanisms, including validation rules, outlier detection, and completeness and timeliness monitoring. However, as national implementations scale in size, complexity, and number of data sources, these capabilities are often experienced as fragmented across applications and workflows. Extending data quality logic frequently requires developing and maintaining dedicated DHIS2 applications, increasing technical overhead and limiting agility. In Ethiopia, the Ministry of Health identified the need for a complementary system level data quality platform that could provide independent assurance while remaining adaptable to evolving national requirements. An earlier national solution demonstrated the value of centralised assessment but faced challenges related to scalability and automation. Building on these lessons, HABTech developed Meskot, a next generation platform designed to operationalise data quality as a continuous system capability. Meskot adopts an automated rule driven validation model that separates rule definition, execution, and result consumption into a coherent lifecycle. Rather than embedding logic in application specific code, the platform provides a pluggable framework in which data quality rules and assessment metrics can be introduced through configuration and well defined interfaces. This allows validation logic to evolve incrementally without developing new applications while maintaining consistency, traceability, and governance. Operating alongside but outside DHIS2, Meskot supports independent validation, cross source triangulation, and temporal and relational checks. Validation outcomes are structured and explainable, enabling systematic feedback, trend analysis, and integration into existing data review and decision making workflows. This approach strengthens transparency, supports learning, and reinforces accountability across national health data systems.
Primary Author: Tigabu Dagne
Keywords:
Data quality DHIS2 Automated validation Health information systems System-level validation Pluggable architecture Rule-driven validation Meskot
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