By forecasting demand and correcting for missing data, researchers from Wharton and Penn Engineering developed a low-cost AI tool that helps get life-saving medicines to the communities in Sierra Leone that need them most.
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 five, 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 Tsai-Hsuan 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 two million women and children under five. The system runs on only $30 per month in server costs and requires no additional workforce.
Above: Connaught Hospital in Sierra Leone (credit: mtcurado via Getty Images)
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