Resources

Immunization is one of the most impactful and cost-effective public health interventions available, saving up to 4 million lives every year, according to the World Health Organization (WHO). In addition to its direct impact on the health of populations, immunization brings added value by reducing the burden of disease on individuals, families, and communities.

In Nigeria, Community Health Extension Workers (CHEWs) are essential to providing frontline health care, particularly in rural and underserved areas where physician shortages are most pronounced. However, they face a significant challenge in their limited access to real-time, comprehensive information that could support timely, accurate decision-making for a wide range of health concerns. Studies have shown that poor utilization of primary health care facilities in Nigeria is often attributed to perceived poor quality care, with CHEWs sometimes struggling to access the necessary information to manage patients effectively (Luka-Lawal et al. 2020). Existing research indicates that CHEWs can deliver safe and effective care when given adequate support. A study conducted in South-South Nigeria found that CHEWs had good results when given access to physician consultation via telephone (Ordinioha and Onyenaporo 2010). These findings suggest that providing decision-support resources enhances the ability of CHEWs to manage cases at the community level.Given the demonstrated benefits of telephonic physician support, this study explores whether a system driven by artificial intelligence (AI) could serve a similar role in enhancing CHEW decision-making capabilities. Unlike physician-supervised telephone consultations, which require ongoing human resources, a Large Language Model (LLM)-based system could provide scalable, on-demand access to health care knowledge without relying on direct physician availability. By integrating LLMs into the CHEW workflow, this study seeks to evaluate the feasibility of AI-driven decision support in improving patient outcomes, reducing unnecessary referrals, and strengthening primary health care service delivery in Nigeria.

From November 2023 through May 2025, perennial malaria chemoprevention was piloted in Kongo Central province in the Democratic Republic of the Congo for malaria prevention in children under two. These briefs summarize the lessons learned from pilot implementation, including areas such as integration into existing systems, supply management, and supervision.

From November 2023 through May 2025, perennial malaria chemoprevention was piloted in Kongo Central province in the Democratic Republic of the Congo for malaria prevention in children under two. These briefs summarize the lessons learned from pilot implementation, including areas such as integration into existing systems, supply management, and supervision.

Cold chain maintenance workshop report: PATH, along with JSI, Village Reach, and CHAI, supported a workshop for over 50 cold chain technicians from six countries to gather and share best practices, engage in peer-to-peer learning, and explore ways to use data for improved cold chain maintenance and decision-making. The workshop report and results from a six-month follow-up survey can be found below.