How Low-Cost AI Is Transforming Healthcare Logistics
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, West Africa, 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.
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 Tsai-Hsuan Chung traveled to the capital city of Freetown. “Local officials were worried that an AI tool arriving from abroad might replace their jobs or leave them responsible if something went wrong,” Tsai-Hsuan Chung says.
To secure local buy-in and gain trust, Tsai-Hsuan Chung 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.”
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.
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 Tsai-Hsuan Chung traveled to the capital city of Freetown. “Local officials were worried that an AI tool arriving from abroad might replace their jobs or leave them responsible if something went wrong,” Tsai-Hsuan Chung says.
To secure local buy-in and gain trust, Tsai-Hsuan Chung 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.”