Improving access to essential medicines via decision-aware machine learning.

Authors

Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh, Lawrence Sandi, Francis Smart, Hamsa Bastani, Osbert Bastani

Published in

Nature. Apr 29, 2026. Epub Apr 29, 2026.

Abstract

A critical challenge in healthcare systems in low- and middle-income countries is the efficient and equitable allocation of scarce resources, particularly essential medicines¹. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques^2-5. Here we propose a novel decision-aware machine learning framework for the allocation of essential medicines, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the national government of Sierra Leone, 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 two million women and children under 5 years of age. Our work demonstrates how machine learning methods can improve efficiency at very low cost in resource-constrained global health settings.

PMID:
42056524
Bibliographic data and abstract were imported from PubMed on 30 Apr 2026.

Read full publication at:
Please sign in to see all details.

Stats

1-terrible, 9-excellent. How would you rate this publication? Sign in in to submit your rating.

Post a comment

You need to be signed in to post comments. You can sign in here.

Comments

There are no comments yet.