Machine learning improves health-care access in Sierra Leone

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29 April 2026 Machine learning improves health-care access in Sierra Leone A machine-learning tool that allocates scarce medicines to meet demand and reduce waste is providing millions with better health care as it rolls out nationwide. By Ziad Obermeyer 0 Ziad Obermeyer Ziad Obermeyer is in the School of Public Health, University of California, Berkeley, Berkeley, California 94720, USA. View author publications PubMed Google Scholar

Scarcity shapes nearly every aspect of health-care delivery in low- and middle-income countries (LMICs), so making good decisions about how to allocate resources is crucial. Yet the tools used to guide these choices are themselves scarce. Methods for forecasting demand, coordinating supply chains and distributing resources, which are taken for granted in high-income settings, are often unavailable or unreliable in LMICs. Writing in Nature, Chung et al.1 describe a machine-learning system for allocating scarce medicines. It works by forecasting demand for medical supplies — how much medication will be consumed, in what clinic, on what day — and using these predictions to guide distribution decisions. Rather than simply offering an algorithmic proof-of-concept, the paper reports a decision engine that is designed for and implemented in the messiness of real-world health systems.

doi: https://doi.org/10.1038/d41586-026-01152-0

References Chung, A. T.-H. et al. Nature https://doi.org/10.1038/s41586-026-10433-7 (2026). Glickman, S. W. et al. N. Engl. J. Med. 19, 816–823 (2009). Ruamviboonsuk, P. et al. Lancet Digit. Health 4, 235–244 (2022). Nisingizwe, M. P. et al. Lancet Glob. Health 10, 564–569 (2022). Download references

Competing Interests The author declares no competing interests.

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