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Updated: May 1, 2026

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Improving access to essential medicines via decision-aware machine learning.

Angel Tsai-Hsuan Chung1, Jatu Abdulai2, Patrick Bayoh2

  • 1Department of Operations, Information and Decisions, University of Pennsylvania, Philadelphia, PA, USA. angelchg@wharton.upenn.edu.

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Summary

A novel machine learning framework improved essential medicine allocation in Sierra Leone, increasing access by 19%. This cost-effective solution enhances healthcare in resource-constrained global health settings.

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Area of Science:

  • Global Health
  • Health Systems Research
  • Machine Learning Applications

Background:

  • Resource allocation in low- and middle-income countries is challenged by data limitations.
  • Efficient and equitable distribution of essential medicines is critical.

Purpose of the Study:

  • To develop and evaluate a novel machine learning framework for essential medicine allocation.
  • To improve efficiency and equity in resource-constrained healthcare settings.

Main Methods:

  • A decision-aware machine learning framework incorporating multi-task learning and catalytic priors.
  • Nationwide deployment in Sierra Leone as a decision support tool.
  • Econometric evaluation of the system's impact on medicine consumption.

Main Results:

  • An estimated 19% increase in consumption of allocated essential medicines in treated districts.
  • Successful nationwide scaling of the system.
  • Demonstrated efficacy in improving access to essential medicines.

Conclusions:

  • Machine learning can significantly improve the efficiency and equity of essential medicine allocation.
  • The developed framework offers a cost-effective solution for resource-constrained global health settings.
  • The system was successfully scaled to cover millions of women and children.