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

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Information Systems (EMCIS 2023)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 501))

  • 108 Accesses

Abstract

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.

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Notes

  1. 1.

    https://community.dhis2.org/t/integrated-canreg5-and-dhis2-improving-cancer-data-management-in-rwanda/52854.

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Author information

Authors and Affiliations

  1. HISP Centre, University of Oslo, Gaustadalléen 30, 0373, Oslo, Norway

    Josue Kuika Watat

  2. Ural Federal University, ul. Mira 32, 620002, Yekaterinburg, Russia

    Ebenezer Agbozo

Authors
  1. Josue Kuika Watat
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  2. Ebenezer Agbozo
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Corresponding author

Correspondence to Josue Kuika Watat .

Editor information

Editors and Affiliations

  1. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  2. University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  3. British University in Dubai, Dubai, United Arab Emirates

    Khalid Al Marri

  4. Dubai Blockchain Center, Dubai, United Arab Emirates

    Marwan Al Zarouni

Appendix - Acronyms

Appendix - Acronyms

  • SIA - Supplemental immunization activity

  • ART- antiretroviral treatment

  • IDSR - Integrated Disease Surveillance and Response

  • RI – Routine Immunization

  • RMNCH - reproductive, maternal, newborn and child health (RMNCH)

  • CHCs - community health centers

  • CHPs - community health posts

  • MCHPs - maternal and child health posts

  • DHIMS - District Health Information Management System

  • CC - community clinics

  • NCD - non-communicable diseases

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Kuika Watat, J., Agbozo, E. (2024). Using Computational Knowledge Extraction Approach to Assess Three Decades of Health Management Information Systems for Informed Actions. In: Papadaki, M., Themistocleous, M., Al Marri, K., Al Zarouni, M. (eds) Information Systems. EMCIS 2023. Lecture Notes in Business Information Processing, vol 501. Springer, Cham. https://doi.org/10.1007/978-3-031-56478-9_18

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  • DOI: https://doi.org/10.1007/978-3-031-56478-9_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-56477-2

  • Online ISBN: 978-3-031-56478-9

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