# Improving Access to Essential Medicines via Decision-Aware Machine Learning

arXiv: https://arxiv.org/abs/2607.20542 · HTML: https://mlsanity.com/paper/2607.20542/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 1 of its 84 references that are in the map.
- First posted on arXiv 10 Jul 2026.

## Builds on (most cited first, 1 of 1)

- [End-to-End Constrained Optimization Learning: A Survey](https://mlsanity.com/paper/2103.16378/) (2021)

## Abstract (arXiv)

A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool. Our econometric evaluation finds an estimated 19% increase in consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2 million women and children under five. Our work demonstrates how machine learning methods can improve efficiency at very low cost in resource-constrained global health settings.