# Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child Health

> **NIH NIH R01** · UNIV OF MARYLAND, COLLEGE PARK · 2021 · $597,635

## Abstract

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.

## Key facts

- **NIH application ID:** 10173272
- **Project number:** 1R01MD016037-01
- **Recipient organization:** UNIV OF MARYLAND, COLLEGE PARK
- **Principal Investigator:** QUYNH NGUYEN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $597,635
- **Award type:** 1
- **Project period:** 2021-09-24 → 2026-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10173272

## Citation

> US National Institutes of Health, RePORTER application 10173272, Rosie the Chatbot: Leveraging Automated and Personalized Health Information Communication to Reduce Disparities in Maternal and Child Health (1R01MD016037-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10173272. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*