Current Journal of Applied Science and Technology

Electronic health record (EHR) systems have been adopted at an accelerating pace across low- and middle-income countries (LMICs) as part of broader efforts to digitise health information management. The data these systems generate underpin clinical care, programme monitoring and national reporting obligations, yet their quality remains uneven and, in many settings, inadequately governed. This review synthesises the published evidence on quality control frameworks applied to EHR and related routine health information system (RHIS) data workflows in resource-constrained settings, drawing on conceptual data quality models, empirical facility-level assessments, and policy and governance literature. It traces the evolution of data quality thinking from generic information systems theory to health-sector-specific frameworks, and examines how completeness, accuracy, timeliness, consistency and confidentiality are operationalised across diverse health system contexts. Structural constraints, including unreliable electricity, limited connectivity, workforce shortages and weak supervisory feedback loops, recur as dominant determinants of poor data quality, while interoperability gaps and fragmented governance arrangements compound the problem at a systems level. Promising developments include offline-capable architectures, structured data quality review toolkits, and early applications of machine learning for anomaly detection, although evidence of their sustained impact in resource-constrained settings remains limited. The review concludes that durable improvement in EHR data quality depends less on technology procurement than on aligning organisational incentives, workforce capacity, governance arrangements and infrastructure investment around a coherent, locally adapted quality assurance framework. Implications for global health information management practice and policy are discussed alongside priority areas for future research.