Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget
🤖 AI Summary
Ethiopia faces challenges in simultaneously achieving universal health coverage breadth and regional equity under budget constraints and dynamic demographic shifts. This paper introduces the Health Access Resource Planner (HARP), a decision-support framework integrating online optimization, learning-augmented algorithms, and a multi-step greedy strategy to maximize population coverage while satisfying region-specific, stage-wise proportional constraints. Its key innovations include: (i) the first integration of domain-expert knowledge into a learning-augmented mechanism—ensuring both interpretability and long-term near-optimality; and (ii) support for rolling-horizon, phased health facility planning. Validated in collaboration with Ethiopia’s Ministry of Health and Public Health Institute, HARP significantly improves resource allocation efficiency and spatial equity across diverse regions. The framework provides a scalable, generalizable methodology for strengthening dynamic health systems in low- and middle-income countries.
Application Category
📝 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 Planner (HARP), based on a principled decision-support optimization framework for sequential facility planning that aims to maximize population coverage under budget uncertainty while satisfying region-specific proportionality targets at every time step. We then propose two algorithms: (i) a learning-augmented approach that improves upon expert recommendations at any single-step; and (ii) a greedy algorithm for multi-step planning, both with strong worst-case approximation estimation. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we demonstrated the empirical efficacy of our method on three regions across various planning scenarios.
Problem
Research questions and friction points this paper is trying to address.
Optimizing health facility prioritization under budget constraints
Maximizing population health coverage with sequential planning
Achieving region-specific proportionality targets in healthcare access
Innovation
Methods, ideas, or system contributions that make the work stand out.
Learning-augmented optimization for health coverage
Greedy algorithm for multi-step budget planning
Proportionality targets under budget uncertainty
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