Data Warehousing for Optimizing Healthcare Resource Allocation in Botswana
Abstract
Healthcare resource allocation remains a persistent challenge in Botswana, primarily due to inefficiencies in data management that obstruct equitable distribution and evidence-based decision-making. Traditional allocation approaches in Botswana exhibit severe fragmentation, low interoperability, and an absence of real-time data analytics factors that contribute to service delivery disparities, especially in rural and underserved areas. In contrast, developed countries have leveraged data warehousing to optimize healthcare resource planning, offering Botswana a proven yet untapped strategic opportunity. This study designs and validates a context-sensitive data warehouse methodology, applying the Kimball Lifecycle model as the guiding framework. A mixed-methods design was adopted, incorporating qualitative interviews with 24 healthcare practitioners and administrators across public and private health facilities, along with quantitative surveys assessing the state of 12 existing health data systems. Results reveal systemic shortcomings in data accuracy (average error rates of 22%), timeliness (with a median data update lag of 14 days), and accessibility (only 38% of facilities had centralized access). Post-implementation of the prototype data warehouse, significant improvements were noted: data accuracy increased by 47%, data accessibility across departments rose to 85%, and decision turnaround time was reduced by 33%. The warehousing also demonstrated cost-effectiveness, reducing redundant data handling expenses by an estimated 18% over six months. In conclusion, this study presents a robust, scalable, and locally adaptable data warehousing framework that effectively addresses Botswana’s systemic challenges in healthcare resource allocation.
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