Nigeria

Community health extension workers are testing the performance of a toll-free platform that uses large language models to deliver on-demand, expert-level responses to their diagnosis and treatment questions.

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.

The integrated post-validation/verification surveillance (PVS) toolkits for neglected tropical diseases (NTDs) were developed as a part of the Bill & Melinda Gates Foundation-funded NTD Surveillance Planning Toolkit project (2022 to 2025). The project aimed to develop and pilot a toolkit for assessing the capacity for integrated disease surveillance and response in low- and middle-income countries to enhance capacity to detect signals and respond to potential recrudescence of NTDs that have been eliminated.The three resources on this page summarize findings and experiences in project countries Bangladesh, Nigeria, and Senegal.

Lymphatic Filariasis and Onchocerciasis reached the World Health Organization’s (WHO) elimination threshold in both Plateau and Nasarawa States in north-central Nigeria in 2017 and 2021, respectively.However, no evidence-informed plan was created to detect or respond to potential recrudescence of the eliminated diseases while waiting for national elimination to occur. To meet this gap, Plateau and Nasarawa States were selected to pilot the development of the WHO integrated post validation/verification surveillance (PVS) planning toolkit for neglected tropical diseases.PATH conducted the pilot, working in collaboration with The Carter Center – Nigeria as a local partner.

The ORS and zinc: A policy and advocacy primer outlines the current global status of ORS and zinc uptake and key actions to increase their scale-up, as well as advocate and communicate about improving access and use.PATH, with funding from the United States Agency for International Development and the Bill and Melinda Gates Foundation, reviewed literature and existing data sources and carried out 37 key informant interviews with global- and country-level stakeholders from five countries: Burkina Faso, Ethiopia, Kenya, Nigeria, and Pakistan.The findings summarized in this primer include key actions to scaling ORS and zinc, strategies for successful implementation, and priority areas for investment to advance progress.

Bilal Mateen is PATH’s inaugural Chief AI Officer. He has been at the forefront of the debate around appropriate applications of artificial intelligence for health for many years, and more recently a vocal advocate among global leaders discussing this topic at the G20.

Many countries struggle to provide high-quality primary health care and achieve universal health coverage because of a shortage of trained clinicians. Consequently, citizens experience a variable standard of care depending on the level of training and experience of the health care workers in their community. Appropriate and user-friendly tools are needed that can provide on-demand, expert-level information and support to frontline health care workers to standardize care and treat more patients effectively.Can artificial intelligence-assisted tools support better primary health care delivery? Artificial intelligence (AI)-based tools are increasingly being used to assist health care workers with diagnosis and treatment. These include large language model (LLM)-enabled tools that can provide expert-level medical knowledge and advice in response to health care workers’ queries. Because LLMs have the ability to be trained to think like a local medical expert, continuously learn, and provide responses to a wide range of queries, this technology is seen to have the potential to enhance clinical decision support systems (CDSSs) and improve the quality of primary health care.

Les ambassadeurs de l'auto-injection prônent pour un accès accru aux contraceptifs auto-injectables.

Self-Injection Ambassadors reflect on their advocacy for expanded access to contraceptive self-injection.

Agbons Oaiya has seen many health information systems fail to reach their promise. Today, he is using his experience to ensure Nigeria's health information systems are integrated, scalable, sustainable, and backed by effective policies and leadership.