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

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.