A Large Language Model (LLM) Tool to Support Frontline Health Workers in Low-Resource Settings

Nirmal Ravi of EHA Clinics Ltd. in Nigeria will develop and test scalable and cost-effective ways to use large language models (LLMs) such as ChatGPT-4 to provide “second opinions” for community health workers (CHEWs) in low- and middle-income countries (LMICs). These second opinions would mirror what a reviewing physician might advise the provider in question after seeing or hearing their initial report. If LLMs can enhance the capabilities of CHEWs in this way, it could improve patient outcomes, free high-skill providers for other tasks, and mitigate the serious shortage of qualified health personnel in many LMICs. The specific outcomes of this project will be: a proof of concept that LLMs can be integrated within LMIC healthcare systems to improve quality of care; a proof of concept of a system architecture for LLMs that can be scaled up and deployed progressively in LMIC healthcare systems; and an initial understanding of the capacity of current LLMs to interact with CHEWs in LMIC settings.