Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
arXiv:2509.00135v2 Announce Type: replace Abstract: As part of nationwide efforts aligned with the United Nations' Sustainable Development Goal 3 on Universal Health Coverage, Ethiopia's Ministry of Health is strengthening health posts to expand access to essential healthcare services. However, only a fraction of this health system strengthening effort can be implemented each year due to limited budgets and other competing priorities, thus the need for an optimization framework to guide prioritization across the regions of Ethiopia. In this paper, we develop a tool, Health Access Resource Plan
The paper leverages a learning-augmented approach to optimize health coverage, reflecting increased interest in applying AI to public health challenges and resource allocation in developing nations.
This initiative demonstrates a practical application of AI/optimization techniques to improve health access in a large developing country, potentially serving as a model for other nations striving for universal health coverage.
The use of an optimization framework (Health Access Resource Plan) could lead to more efficient allocation of limited health resources in Ethiopia, improving access to essential services.
- · Ethiopian Ministry of Health
- · Ethiopian population
- · AI for social good initiatives
- · Public health organizations
- · Inefficient resource allocation practices
Improved healthcare access and outcomes in Ethiopia for key regions.
Potential for replication of this optimization model in other developing countries facing similar public health challenges.
Increased global focus on AI-driven solutions for achieving Sustainable Development Goal 3, potentially drawing more investment and research into this area.
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Read at arXiv cs.AI