ArticleOxford open digital health2026
Data warehousing in healthcare: enhancing resource allocation and decision-making in Botswana.
Article in Oxford open digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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3 authors.
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Abstract
Healthcare systems in low- and middle-income countries (LMICs) face persistent challenges in resource allocation due to fragmented health information systems and limited decision support capacity. In Botswana, despite significant investments in platforms such as District Health Information Software, Integrated Patient Management System, and Patient Information Management System, these systems remain siloed, constraining real-time analytics and equitable planning. Existing literature has primarily emphasized isolated platforms or high-level policy discussions, leaving a critical gap in empirically grounded, methodologically rigorous frameworks for integrating disparate datasets into unified, decision-oriented architectures. This study addresses that gap by designing and evaluating a Kimball-based dimensional data warehousing framework tailored to Botswana's healthcare ecosystem. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, a systematic review of 72 peer-reviewed articles published between 2021 and 2025 was conducted, focusing on modeling approaches, interoperability practices, and operational outcomes. Results reveal a clear preference for Kimball's bottom-up dimensional modeling, particularly star schemas, due to their incremental deployment, user-centered design, and suitability for resource-constrained environments. Thematic synthesis highlights medicine stockouts, staffing mismatches, and budget execution delays as recurring bottlenecks, with evidence that robust ETL pipelines and source-to-target mapping significantly improve data reliability and decision-making. Findings demonstrate that a localized Kimball framework can transform fragmented health data into actionable intelligence, enabling real-time visibility of staffing, inventory, and financial flows. The study concludes that such an approach not only strengthens efficiency and equity in Botswana's healthcare system but also offers a transferable model for other LMICs seeking to align technical innovation with governance realities and operational needs.
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