# Health Facility Location in Ethiopia: Leveraging LLMs to Integrate Expert Knowledge into Algorithmic Planning

arXiv: https://arxiv.org/abs/2601.11479 · HTML: https://mlsanity.com/paper/2601.11479/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 36 references that are in the map.
- First posted on arXiv 16 Jan 2026.

## Builds on (most cited first, 10 of 10)

- [Deep reinforcement learning from human preferences](https://mlsanity.com/paper/1706.03741/) (2017)
- [A Survey of Multi-Objective Sequential Decision-Making](https://mlsanity.com/paper/1402.0590/) (2014)
- [AdaPlanner: Adaptive Planning from Feedback with Language Models](https://mlsanity.com/paper/2305.16653/) (2023)
- [Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks](https://mlsanity.com/paper/2503.09572/) (2025)
- [AgentOccam: A Simple Yet Strong Baseline for LLM-Based Web Agents](https://mlsanity.com/paper/2410.13825/) (2024)
- [A Decision-Language Model (DLM) for Dynamic Restless Multi-Armed Bandit Tasks in Public Health](https://mlsanity.com/paper/2402.14807/) (2024)
- [Balancing Act: Prioritization Strategies for LLM-Designed Restless Bandit Rewards](https://mlsanity.com/paper/2408.12112/) (2024)
- [Navigating the Social Welfare Frontier: Portfolios for Multi-objective Reinforcement Learning](https://mlsanity.com/paper/2502.09724/) (2025)
- [Optimizing Health Coverage in Ethiopia: A Learning-augmented Approach and Persistent Proportionality Under an Online Budget](https://mlsanity.com/paper/2509.00135/) (2025)
- [VORTEX: Aligning Task Utility and Human Preferences through LLM-Guided Reward Shaping](https://mlsanity.com/paper/2509.16399/) (2025)

## Abstract (arXiv)

Ethiopia's Ministry of Health is upgrading health posts to improve access to essential services, particularly in rural areas. Limited resources, however, require careful prioritization of which facilities to upgrade to maximize population coverage while accounting for diverse expert and stakeholder preferences. In collaboration with the Ethiopian Public Health Institute and Ministry of Health, we propose a hybrid framework that systematically integrates expert knowledge with optimization techniques. Classical optimization methods provide theoretical guarantees but require explicit, quantitative objectives, whereas stakeholder criteria are often articulated in natural language and difficult to formalize. To bridge these domains, we develop the Large language model and Extended Greedy (LEG) framework. Our framework combines a provable approximation algorithm for population coverage optimization with LLM-driven iterative refinement that incorporates human-AI alignment to ensure solutions reflect expert qualitative guidance while preserving coverage guarantees. Experiments on real-world data from three Ethiopian regions demonstrate the framework's effectiveness and its potential to inform equitable, data-driven health system planning.