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Data from: Improving access to essential medicines via decision-aware machine learning

Chung, Angel Tsai-Hsuan1; Abdulai, Jatu2; Bayoh, Patrick2; Sandi, Lawrence2; Smart, Francis3; Bastani, Hamsa1; Bastani, Osbert1

Published Mar 16, 2026 on Dryad. https://doi.org/10.5061/dryad.h9w0vt4tw

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Mar 16, 2026 version files 50.31 MB

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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 and evaluated its impact using synthetic difference-in-differences. We find an estimated 19% increased 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.