Mercy C. Terer is a PhD student at Liverpool School of Tropical Medicine (LSTM) and holds a Bachelor of Science in Computer Science and Biostatistics. She previously served as a Data Manager at Kenya Medical Research Institute-Wellcome Trust’s Clinical Information Network, where she played a key role in establishing a multi-country pragmatic randomised controlled trial that compared treatment strategies for severe pneumonia in children across Kenya, Uganda, and Tanzania.

Her innovative work led to the development of an SMS-based randomisation platform, an achievement that earned her first prize in the 2019 Global Health Methodology Research Competition. She later secured a grant for the pilot implementation of short message service for randomisation in a multisite pragmatic factorial clinical trial in Kenya, demonstrating the feasibility of SMS randomisation in resource-limited settings.

Mercy also worked as a Senior Data Management Officer for the Attractive Targeted Sugar Bait and MiMBa Pregnancy Registry studies at Professor Sarah Staedke is a clinical epidemiologist with specialist training in infectious diseases and global public health. She studied medicine at the University of Texas Southwestern Medical School and completed her clinical training in Internal Medicine (University of Colorado) and Infectious Diseases (University of California, San Francisco) before earning a PhD from the London School of Hygiene & Tropical Medicine. Prof Staedke was previously based in Uganda with the Infectious Diseases Research Collaboration (1999-2022) and was on faculty at London School of Hygiene & Tropical Medicine from 2006-2022. She joined LSTM in 2022 as a Professor of Malaria & Global Health, based at Kenya Medical Research Institute’s Centre for Global Health Research in Kisumu, western Kenya.

Her PhD research focuses on leveraging machine learning techniques to improve maternal health data linkage in pregnancy registries, particularly in optimising gestational age determination for enhanced data accuracy. She is supervised by Dr. Stephanie Dellicour, Prof. Feiko ter Kuile, Prof. Miriam Taegtmeyer, and Prof. Maia Lesosky.

In addition, she occasionally serves as an Assistant Data Protection Officer for Kenya Medical Research Institute/Liverpool School of Tropical Medicine (LSTM) collaborations and is a journal reviewer for PLOS Digital Health, specialising in machine learning, digital health systems, and data governance.

Data is fundamental to any research outcome and subsequent interventions, which is where Mercy’s interests lie. In routine healthcare settings, digital health systems collect vast amounts of data daily. However, much of this rich data is underutilised, often limited to routine indicator reporting rather than being leveraged for research. While data quality challenges exist, their value can only be fully realized through proper utilisation.

Mercy’s focus is on shaping how digital patient care data is structured – both for research and improved patient care. Currently, she is working on record linkage, developing machine learning algorithms to optimise the use of pregnancy registry datasets in routine care settings. Gestational age estimation is crucial for maternal and child healthcare, and enhancing its accuracy improves linkage methods. Her goal is to demonstrate how machine learning can provide a scalable, automated approach to enhancing data quality in maternal health research and routine care.

As data generation continues to grow and also the adoption of AI in Africa, Mercy also has a strong interest in the region’s AI governance landscape, particularly in its implementation within public health research.