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Preventable maternal, newborn, and child deaths remain unacceptably high. After decades of progress, mortality rates have stagnated. The targets set by leaders around the world to reduce maternal, newborn, and child deaths by 2030 are currently out of reach for many countries. However, with renewed commitment and strategic actions, we can get back on track.This brief from PATH's Center for Advocacy and Policy explores five key calls to action to address urgent maternal, newborn, and child health (MNCH) advocacy priorities and accelerate progress. It is intended for advocates, technical decision-makers, policymakers, and anyone who wants to learn about key MNCH advocacy approaches that can help drive sustainable improvements in health for women and children.

A lack of formal clinical training often limits the effectiveness of community health workers (CHWs) and, thus, their ability to generate timely and accurate insights based on the information shared with them. Integrating large language models (LLMs) into the CHWs’ workflow presents a novel opportunity to enhance their decision-making capabilities by providing real-time, context-specific, and evidence-based guidance. This non-interventional study aims to explore whether LLMs can support more effective decision-making. After an interaction between a CHW and a patient, the LLM will analyze the recorded conversation to determine the appropriateness of referral decisions made by CHWs compared to the LLM against the clinical expert panel consensus. The study also assesses the feasibility and appropriateness of an LLM-based clinical decision support system for CHWs. The results will inform future research, including the need for an interventional trial of such a product.

PATH partners with governments, civil society organizations, and advocates to advance health equity by informing evidence-based policymaking and funding decisions. We leverage technical expertise and deep relationships from country to global levels to design and shape impactful policies, ensure effective implementation, and strengthen capacity along the way.

This study explores whether an LLM, a type of artificial intelligence (AI) that processes and generates text in response to written questions and prompts, can help improve clinician decision-making in Kenya. LLMs have the potential to assist clinicians in making more evidence-based and informed decisions, especially when time and resources are limited. This study will evaluate the effectiveness of an LLM-based clinical decision support tool at Penda Health, a primary health care provider in Kenya.The study aims to evaluate if using an LLM can help reduce the number of patients who need to return to health care providers for unresolved health problems or need emergency care. We are also evaluating whether this tool can improve care for certain conditions, such as high blood pressure, diabetes, malnutrition in young children, and antibiotic prescribing in infectious diseases. Since these conditions are common but often go untreated or misdiagnosed, providing clear and accurate information to health care workers could make a significant difference.To test this, patients visiting Penda Health clinics will be assigned to one of two groups. In one group, clinicians will use the LLM to support their decisions and clinicians in the other group will not use the LLM. After their visits, patients will be contacted on days 3 and 14 to check if their symptoms have improved, if they had to seek additional care, or if they had other safety concerns. We will also collect information about how satisfied patients felt with their care.An independent panel of medical experts will review how well the LLM’s advice matched safe and effective clinical practices. We will determine if using the LLM influences health care workers’ decisions to refer patients for more advanced care. Additionally, we will review changes in the frequency of antibiotics and malaria medication prescriptions. Finally, we will look at how patients feel about the LLM-assisted care compared to regular care, particularly in terms of clarity and thoroughness.By the end of this study, we hope to understand how well this AI tool works in primary care settings in Kenya and whether it can safely support health care workers and improve patient care.

This brief provides key data, insights, and policy recommendations to strengthen Africa’s mpox diagnostic capacity. It explores the challenges of limited testing access, the need for locally manufactured diagnostics, and the role of innovative point-of-care solutions in improving outbreak response. The information in this brief is valuable for anyone looking to support or understand efforts to expand diagnostic access through policy, research, funding, or implementation. By integrating diagnostics into national and regional health strategies, Africa can build a more resilient, prepared, and responsive health system.