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

TL;DR

This paper introduces a hybrid framework combining optimization algorithms and large language models to improve health facility planning in Ethiopia, effectively integrating expert knowledge with data-driven methods.

Contribution

It presents the LEG framework that merges approximation algorithms with LLM-driven iterative refinement for health facility prioritization.

Findings

Effective integration of expert knowledge with optimization algorithms.

Demonstrated success on real-world Ethiopian health data.

Potential to enhance equitable health system planning.

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.