PROJECT SUMMARY/ABSTRACT This proposal has the potential to alter the way health information is presented to vulnerable populations. Our proposal promotes a more flexible and tailored approach to reach underserved groups. Racial/ethnic minority women are at increased risk for postpartum depression, and their children as less likely to have had well-child checkups in the past year. Moreover, racial/ethnic disparities are still prevalent for maternal and infant mortality as well as various health behaviors such as safe sleep practices, breastfeeding, and infant nutrition. Currently, some popular programs involve resource-intensive home visits (limited in scale due to staff and cost constraints) or non-personalized text messages (may not directly address an individual’s questions). We propose the development of a chatbot that addresses both of these possible limitations by representing a scalable tool that can have widespread reach across geographies and is personalized and responsive to an individual’s specific informational needs. We have built a prototype of the chatbot, Rosie, capable of engaging in live question-and-answer sessions. Rosie is able to respond to 334 popular questions that new mothers may have. Pretests with mother groups and Mary’s Center patients have showed a positive reception to the chatbot. Over the course of the grant, we will leverage recent advances in natural language processing and the emergence of efforts to aggregate massive amounts of health information, to assemble a comprehensive health information library. We will further refine Rosie’s dialogue analyzer and response inference engine to robustly recognize and respond to user’s questions in the various and complex ways they can phrase a question. We will test the hypothesis that Rosie may lower risk of postpartum depression, decrease emergency room visits, and increase attendance of well-baby visits. We will employ primarily a virtual recruitment strategy to conduct a randomized controlled trial to evaluate the impact of this intervention on maternal and infant outcomes. Our investigative team—comprised of experts in the field of epidemiology, computer science, biostatistics, and maternal and child health experts—is uniquely suited to implement the study aims. Our Specific Aims are: 1) Develop technology for a chatbot, Rosie, that will provide health informational support to vulnerable mothers the moment they need it; 2) Evaluate the use of Rosie on maternal and infant outcomes; and 3) Release an open-source packet for the construction of a chatbot. Rosie provides informational support to vulnerable moms the moment they need it and safeguards new moms from misinformation that is common on the web with the ultimate goal of closing the gap in maternal and infant outcomes. Results and tools developed from this proposal can be utilized to inform population-based strategies to reduce health disparities and improve health.