Improving Access to Essential Medicines via Decision-Aware Machine Learning

Abstract: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.

Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Computers and Society (cs.CY)

arXiv:2607.20542 [cs.LG]

(or arXiv:2607.20542v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2607.20542 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

https://doi.org/10.1038/s41586-026-10433-7 Focus to learn more DOI(s) linking to related resources

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