Comparative Effectiveness of DHIS2 and FAIR Data Approaches for Privacy-Preserving Health Data Analytics in Uganda: A Systematic Review

Abstract

Purpose: Uganda’s digital health transformation anchored on District Health Information Software 2 (DHIS2) and the FAIR (Findable, Accessible, Interoperable, Reusable) Data Principles has reshaped health data governance. Nevertheless, systemic constraints in privacy, infrastructure, and human resources threaten sustainability and equity. Objective: To compare DHIS2 and FAIR-based approaches on (i) privacy protection, (ii) interoperability and data usability, and (iii) regulatory/institutional readiness for privacy-preserving health data analytics in Uganda. Methods: Systematic review of 84 peer-reviewed and grey-literature sources (2010–2025) following PRISMA 2020; extracted indicators on reuse, interoperability, privacy, and institutional readiness. Results: 36% of included studies were Uganda-specific; 50% were published in 2020–2024. DHIS2 reached near-national coverage, ~12,000 trained users, and integration across >20 programmes. Persistent gaps include limited rural internet (≈12% of facilities with stable connectivity), high staff turnover (~35%), and low analytics literacy (~25% with intermediate skills). FAIR efforts (since ~2019) remain early: ~10% of institutions with formal policies; low dataset reuse (~22%), machine-readable metadata (~18%), and documented digital consent (<10%). Privacy infrastructure is weak: <30% of facilities with formal privacy frameworks/secure platforms and <10% with Data Protection Officers. Conclusion: DHIS2 improved reporting and availability, while FAIR initiatives began enabling governed, interoperable reuse. To achieve ethical analytics at scale, priorities are legal enforcement, secure rural ICT, standardized machine-readable metadata/consent, and workforce development.