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

2026-01-16

AI Summary

This study addresses the challenge of prioritizing upgrades to rural health posts in Ethiopia under resource constraints to maximize population coverage while accommodating diverse qualitative preferences from experts and stakeholders. The authors propose the LEG framework, which uniquely integrates large language models (LLMs) with a provably approximate facility location algorithm. By leveraging LLMs to interpret expert preferences expressed in natural language and embedding these insights into an extended greedy optimization process, the approach aligns quantitative coverage objectives with qualitative decision-making criteria. Empirical evaluation across three Ethiopian regions demonstrates that the method maintains theoretical approximation guarantees while enabling more equitable, data-driven, and human–AI collaborative health system planning.

Technology Category

• Machine Learning: Learning Preferences or Rankings
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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.

Problem

• Health Facility Location
• Resource Allocation
• Expert Knowledge Integration
• Population Coverage
• Stakeholder Preferences

Innovation

• Large Language Models
• Facility Location
• Human-AI Alignment
• Optimization
• Health Systems Planning