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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
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Intégration des systèmes d'information sanitaire et efficience décisionnelle : étude empirique du DHIS2 dans la métropole de Kinshasa

Authors: Sédrick DOWO NDJATE;

Intégration des systèmes d'information sanitaire et efficience décisionnelle : étude empirique du DHIS2 dans la métropole de Kinshasa

Abstract

The digital transformation of health systems has become a central priority for strengthening health governance and improving evidence-based decision-making. In many low- and middle-income countries, national health information systems have been progressively digitized through platforms such as the District Health Information System (DHIS2). However, the effective integration of health facilities into these systems and the actual use of health data for decision-making remain uneven. In the Democratic Republic of the Congo, particularly in large urban environments such as Kinshasa, the complexity of institutional arrangements and the diversity of healthcare providers pose significant challenges for the organization and utilization of health information systems. This study aims to analyze the organization, integration, and use of health information systems in the urban health system of Kinshasa, with particular attention to the institutional and organizational factors influencing the production and use of health data for decision-making. The research adopts an empirical mixed-methods approach combining quantitative and qualitative data. Administrative data from the 2025 consolidated Operational Action Plan of the Kinshasa Provincial Health Division were analyzed to assess the overall integration of health facilities into the national health information system (DHIS2). In addition, a field survey was conducted in 32 healthcare facilities across eight health zones of Kinshasa. Data were collected through structured questionnaires, semi-structured interviews with health administrators and data managers, and direct observation of data management practices. Quantitative data were analyzed using descriptive statistics, while qualitative data were examined through inductive thematic analysis. An analytical model of decision-making efficiency was also developed to evaluate the relationship between data quality, information system integration, data governance, and the speed of information circulation. The findings show that only 1,634 out of 4,691 health facilities in Kinshasa (35%) are currently integrated into the national DHIS2 platform, limiting the centralization and real-time monitoring of health data. The facility-level survey reveals significant disparities in digital infrastructure: 68.8% of facilities have functional computers, 59.4% have regular Internet access, and only 37.5% have operational access to DHIS2. Data management practices remain hybrid, with 34.4% of facilities relying exclusively on paper-based registers and 43.8% combining paper records with delayed digital entry. Furthermore, only 31.3% of facilities report regular use of health data for planning or management purposes. The proposed decision-making efficiency model highlights that information system integration and data governance are the most influential factors in enabling the effective use of health data within healthcare institutions. The study demonstrates that the digital transformation of health information systems in Kinshasa is constrained not only by technological limitations but also by organizational and governance challenges. Strengthening digital infrastructures, improving data governance mechanisms, and enhancing the analytical capacities of healthcare personnel are essential to increase the effective use of health data in decision-making processes. The analytical framework proposed in this study provides a useful tool for evaluating the performance of health information systems in urban health systems of low- and middle-income countries.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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