Low-cost AI could transform health care logistics in low- and middle-income countries

Managing a medical supply chain in low- and middle-income countries can mean navigating a landscape prone to extreme and unexpected disruptions. In Sierra Leone, for instance, external forces ranging from an attempted military coup and an infectious disease outbreak to a widespread electricity outage can complicate public health logistics.

The consequences are severe. Despite a national government initiative dedicated to providing free medical care and essential supplies to pregnant women and children under 5, Sierra Leone has one of the highest maternal mortality rates in the world, at 717 deaths per 100,000 live births, explains Hamsa Bastani, an operations researcher and statistician at the Wharton School.

A major driver is not always a lack of medicine but a failure to get the right supplies to the right place at the right time, says Bastani. Some clinics end up overstocked while others run dry.

To address that mismatch, Bastani, computer scientist Osbert Bastani and Ph.D. candidate Angel Tsai-Hsuan Chung partnered with Sierra Leone’s government to build a low-cost decision-support system that uses machine learning to forecast demand and optimize how medicines are allocated.

Following a pilot rollout in five districts, the researchers found a 19% increase in consumption of allocated medical products in treated areas, a proxy for improved access. Their findings are published in Nature.

The tool predicts how much of each product individual facilities will likely need and then computes the most efficient way to distribute the limited national stock, explains first author Chung. It is “designed for a setting where data are sparse, noisy, and often incomplete.”

The new system also addresses previous inequities—facilities serving poorer, more remote populations that frequently experienced chronic stockouts saw a 32% surge in medicine consumption with the new tool.

Based on these results, the government scaled the system nationwide. Today, it supports allocation decisions for more than 70 essential products—including medicines to help with postpartum hemorrhaging and treat the seizures of eclampsia, alongside other essentials like tetanus vaccines, gloves and antimalarial medicines—across the country, reaching an estimated 2 million women and children under 5. The system runs on only $30 a month in server costs and requires no additional workforce.

Field work leads to real work

To build a tool capable of handling Sierra Leone’s highly varied logistical ecosystem, the researchers knew they could not rely solely on remote data feeds or Zoom calls, so Chung traveled to the capital, Freetown.

“Local officials were worried that an AI tool arriving from abroad might replace their jobs or leave them responsible if something went wrong,” Chung says.

To secure local buy-in and gain trust, she spent weeks conducting personalized training sessions, ensuring fair compensation for their time. She led the design of a web application that closely mirrored the agency’s preexisting spreadsheet workflows, reducing the friction of forcing workers to learn a complex, alien software system.

“Crucially,” adds Hamsa Bastani, “the system chiefly functions as a ‘decision-support’ tool wherein local officials always retain final say and can override recommendations.”

Under the hood of their AI system

Understaffed and under-resourced clinics are the least able to consistently report data, leading to data gaps clustered around the very places where need is greatest. That leads to a subtle distortion: If a model learns only from the cleanest data, it will favor the best-documented clinics—the ones already better served—while overlooking those where the record is thin but the need is acute.

The team circumvents this bias using multitask learning, which allows the model to borrow shared patterns—like seasonal demand—from places with richer data and apply them where records are sparse.

They paired that with a “backstop” built from external information, including census data and Google Earth images of the vegetation around the clinics, which indicate human activity. This approach helped define catchment areas based on travel time between those areas and the facilities. When those data were combined with census data on the proportion of women and children living within zones, the algorithm could tease out a baseline estimate for how much medicine the clinic needed based purely on the local demographics.

These estimates do not capture every local fluctuation, but they provide a stable baseline tied to population and geography.

Looking ahead

With ownership of the allocation tool now fully transferred to Sierra Leone’s government, the research team is turning outward. Chung is currently working on another project with officials from Somaliland, collaborating with Taiwanese partners to adapt similar data-driven approaches to other regional health systems.

Ultimately, the team hopes their work serves as a blueprint for the future, demonstrating that machine learning can improve health care delivery in resource-constrained environments at low cost.

Who’s behind this story?

Gaby Clark

MA in English, copy editor since 2021 with experience in higher education and health content. Dedicated to trustworthy science news.

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Robert Egan

Bachelor’s in mathematical biology, Master’s in creative writing. Well-traveled with unique perspectives on science and language.

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