Using Computational Knowledge Extraction Approach to Assess Three Decades of Health Management Information Systems for Informed Actions

This research explores the role of the District Health Information System (DHIS2) in global health decision-making. Although DHIS2 is widely used in healthcare systems around the world, there is a significant shortage of in-depth scholarly research assessing its effectiveness and scope. This gap is critical, as understanding DHIS2’s full potential is central in today’s global health context, where data-driven decision-making is fundamental to both managing public health crises and improving healthcare delivery. To address this, our research employs the Latent Dirichlet Allocation (LDA) methodology to analyze a corpus of DHIS2-centric research. By doing so, we aim to uncover the main themes and applications of DHIS2 for informed action, thereby providing a more structured view of its impact in the health data management field. In addition, the study yielded a word cloud which depicted a variety of diseases connected to DHIS2 research, outlining its fundamental value in disease surveillance and management. The results emphasize DHIS2’s enabling role within the broader HMIS ecosystem in developing a data-centric approach for effective public health interventions and management of health emergencies. This study develops a blueprint for exploiting HMIS to its maximum potential for informed public health activities by gaining sophisticated knowledge of DHIS2 through the LDA analysis. It guides researchers in understanding DHIS2’s applications, facilitating further exploration in areas like disease trend analysis. Practitioners can use these insights to improve health surveillance, for instance, by tracking and managing outbreaks, thereby making more informed decisions in public health scenarios.