Date of Award
8-2024
Document Type
Thesis
Degree Name
Master of Science (MS)
Department
Industrial Engineering
Committee Chair/Advisor
Dr. David Neyens
Committee Member
Dr. Emily Tucker
Committee Member
Dr. Emma Dixon
Abstract
High blood pressure, also known as hypertension, significantly increases the risk of heart disease and stroke, which are leading causes of death in the United States. While contributing to over 691,000 deaths in 2021 alone in the United States (U.S.), it also imposes immense economic burden on the healthcare system, costing approximately $131 billion annually. One way to address this issue is for increased self-care behaviors and medication adherence, both of which require sufficient health literacy. Despite the importance of health literacy, 90% of U.S. adults struggle with health-related subjects. Overcoming the issues associated with health literacy requires addressing the inherent challenges of presenting health information as it is often unfamiliar, complicated, and exceptionally technical. However, with the advent of large language models (LLMs) such as ChatGPT, there are new opportunities to deliver health information more effectively and accessibly in the form of chatbots and conversational agents (CAs).
This thesis takes a mixed-methods approach to investigate the effects of varying information presentation styles on a healthcare chatbot’s effectiveness, trust, and usability to assist users in a health-related information seeking task. Controlling for the communication style (conversational or informative) and language style (technical or non-technical), participants engaged with a healthcare chatbot to learn about blood pressure and hypertension. They then engaged in a semi-structured interview detailing their experience and ideating on future health information presentation designs for chatbots and CAs. Hierarchical Bayesian regression models were created to provide inference for how varying information presentations affected the chatbot’s effectiveness, trust, and usability. Moreover, inductive thematic analysis was conducted to analyze the qualitative interviews. The findings revealed dynamic interactions between the various information presentation styles and the outcome measures. Moreover, the qualitative interviews provide evidence for diverse information presentations styles to enhance usability and ease cognitive load for health-related information. These results suggest the importance of tailoring health information presentation to users’ cognitive abilities and preferences to enhance learning of health-related information. As chatbots and other CAs become increasingly used to disseminate health-related subjects, ensuring effective communication and usability will become increasingly more prevalent.
Recommended Citation
Koscelny, Samuel Nelson, "Exploring Healthcare Chatbot Information Presentation: Applying Hierarchical Bayesian Regression and Inductive Thematic Analysis in a Mixed Methods Study" (2024). All Theses. 4342.
https://open.clemson.edu/all_theses/4342
Author ORCID Identifier
0009-0002-7488-6169
Included in
Applied Statistics Commons, Artificial Intelligence and Robotics Commons, Data Science Commons, Design of Experiments and Sample Surveys Commons, Experimental Analysis of Behavior Commons, Graphics and Human Computer Interfaces Commons, Health Communication Commons, Health Information Technology Commons, Human Factors Psychology Commons, Industrial Engineering Commons, Other Statistics and Probability Commons, Probability Commons, Public Health Education and Promotion Commons, Quantitative Psychology Commons, Statistical Models Commons