Angel Tsai-Hsuan Chung

Angel Tsai-Hsuan Chung, Jatu Abdulai, Patrick Bayoh, Lawrence Sandi, Francis Smart, Hamsa Bastani, Osbert Bastani (2026), Improving Access to Essential Medicines via Decision-Aware Machine Learning, Nature (Research Article) (Forthcoming).

Abstract: A critical challenge in healthcare systems in low- and middle-income countries (LMICs) is the efficient and equitable allocation of scarce resources, particularly essential medicines. This problem is complicated by limited high-quality data, which restricts the applicability of traditional data-driven techniques. We propose a novel decision-aware machine learning framework for essential medicines allocation, which additionally leverages multi-task learning to ensure sample efficiency and catalytic priors to ensure equitable allocation. In collaboration with the Sierra Leone national government, we performed a staggered, nationwide deployment of our system as a decision support tool. Our econometric evaluation finds an estimated 19% increase in consumption of allocated products in treated districts, demonstrating its efficacy at improving access to essential medicines. Our tool was subsequently scaled nationwide, covering an estimated 2 million women and children under five. Our work demonstrates how machine learning methods can improve efficiency at very low cost in resource-constrained global health settings.

Hamsa Bastani, Osbert Bastani, Angel Tsai-Hsuan Chung, “Optimizing Health Supply Chains in LMICs with Machine Learning: A Case Study in Sierra Leone”. In Responsible and Sustainable Operations: The New Frontier, edited by Tang, Christopher S. (Switzerland: Springer Nature, 2024), pp. 187-202

Abstract: This chapter overviews the challenges in pharmaceutical supply chains (PSCs) in Low- and Middle-Income Countries (LMICs), with a focus on Sierra Leone. Furthermore, it describes how traditional supply chain optimization strategies can be used to improve performance of PSCs in Sierra Leone. Finally, it describes the significant potential for using machine learning in this framework for effective demand forecasting. We highlight challenges such as limited data availability, the need to ensure equitable distribution, as well as the potential for transfer learning to address some of these challenges.