There are 50 million family caregivers caring for their loved ones in the United States. Caregivers experience significant stress and burnout, and need on-demand support with minimal resources investment. With the burgeoning development of artificial intelligence, conversational agents (chatbots) emerged as a solution for symptom self-management and self-care. Our research team developed Caring for Caregivers Online (COCO) - an AI-enhanced platform providing on-demand, empathetic, and tailored caregiving support. One of the key challenges in designing a chatbot is to ensure effective communication. To address this problem, our research team aims to determine the best practice in conveying health symptoms and solutions in a conversational agent to improve users' understanding of their health data and increase their trust in the technology. User testing and surveys are our main tools to address this question. After the initial session with the chatbot, users were asked to fill out a post-conversation survey. Based on users' ratings on their symptom intensity and solution effectiveness in the post-conversation survey, the chatbot generates personalized health recommendations. Next, we randomly assigned users to one of the two surveys that present the same recommendations in different ways—one with text, the other with a visualization. Users then provided ratings on how well they understood the visualization, their trust toward the chatbot, and some additional feedback. The results of the rating will be analyzed with an average score comparison between the two groups, and qualitative analysis will be employed to evaluate users' feedback. We expect the group presented with visualizations to give higher ratings in areas of both trust and data comprehension. Effective data visualizations inform caregivers of their health progress which motivates them to continue monitoring their health conditions and further practice proposed solutions. In addition, visualization increases caregivers' level of trust in chatbots and helps improve their chronic health conditions.

In the United States, populations with chronic conditions and their families continue to experience a lack of access to quality healthcare despite ongoing advocacy efforts to combat health disparities and policies put into place to eliminate them. Healthcare interventions that are designed to achieve health equity fail because they do not address all of the factors and social determinants that influence health. To address this problem, I will investigate if access to care is associated with health outcome indicators such as quality of life, life expectancy, morbidity and mortality of individuals with chronic conditions from a state policy level and from a global perspective in Cuba. In particular policies for healthcare coverage such as private insurance, employer insurance, medicare, and medicaid. Ultimately, achieving health equity among people with chronic health conditions by working with health government agencies and suggesting policy changes that promote equal access to care. I will conduct a literature review on the lack of access to care in populations with chronic conditions and perform key informants' interviews. Interviews will consist of individuals from Cuba and locally in Washington State. In conducting the literature review and interviews I can anticipate that the results will be a higher expenditure (total cost and out of pocket cost) in healthcare from the United States but a decreased level of access to healthcare. Contrarily, in Cuba there will be a lower expenditure in healthcare but a higher level of access to healthcare.