The Effects of a Healthcare Chatbots\u27 Language and Persona on User Trust, Satisfaction, and Chatbot Effectiveness

Healthcare technology is growing in its capabilities and capacity to impact people’s daily lives. One area of interest for growth is the use of chatbots and other telehealth applications that allow people to receive ubiquitous health information. The benefit of these systems is the ability to give access to pertinent, personalized healthcare information and services that could otherwise be inaccessible for some populations. With personalized information, patients may gain the information needed to make efficacious healthcare decisions which ideally will result in quicker recovery times and lower overall healthcare system costs. Chatbots have already been studied in the healthcare domain as resources for smoking cessation, diet recommendation, and other assistive applications. Yet, few studies have examined the specific design characteristics of healthcare chatbots. My research objective was to analyze two characteristics, language and persona, and their effect on outcomes such as effectiveness, usability, and trust in a chatbot. A between-subject study was performed where participants interacted with a chatbot. Each of chatbot conditions had a language of either technical or non-technical, and persona of Doctor, Nurse, or Nursing Student Sarah. Language was found to have a significant effect on effectiveness, but not trust or usability. In particular, participants who experienced technical language improved significantly greater than those who experienced non-technical language. Persona was found to not be significant for any of the outcomes. Overall, this study demonstrated a need to further study and understand how chatbot design characteristics impact users and how they comprehend the information given to them, particularly from a healthcare perspective

Using a Bayesian Framework to Develop 3D Gestural Input Systems Based on Expertise and Exposure in Anesthesia

Interactions with a keyboard and mouse fall short of human capabilities and what is lacking in the technological revolution is a surge of new and natural ways of interacting with computers. In-air gestures are a promising input modality as they are expressive, easy to use, quick to use, and natural for users. It is known that gestural systems should be developed within a particular context as gesture choice is dependent on the context; however, there is little research investigating other individual factors which may influence gesture choice such as expertise and exposure. Anesthesia providers’ hands have been linked to bacterial transmission; therefore, this research investigates the context of gestural technology for anesthetic task. The objective of this research is to understand how expertise and exposure influence gestural behavior and to develop Bayesian statistical models that can accurately predict how users would choose intuitive gestures in anesthesia based on expertise and exposure. Expertise and exposure may influence gesture responses for individuals; however, there is limited to no work investigating how these factors influence intuitive gesture choice and how to use this information to predict intuitive gestures to be used in system design. If researchers can capture users’ gesture variability within a particular context based on expertise and exposure, then statistical models can be developed to predict how users may gesturally respond to a computer system and use those predictions to design a gestural system which anticipates a user’s response and thus affords intuitiveness to multiple user groups. This allows designers to more completely understand the end user and implement intuitive gesture systems that are based on expected natural responses. Ultimately, this dissertation seeks to investigate the human factors challenges associated with gestural system development within a specific context and to offer statistical approaches to understanding and predicting human behavior in a gestural system. Two experimental studies and two Bayesian analyses were completed in this dissertation. The first experimental study investigated the effect of expertise within the context of anesthesiology. The main finding of this study was that domain expertise is influential when developing 3D gestural systems as novices and experts differ in terms of intuitive gesture-function mappings as well as reaction times to generate an intuitive mapping. The second study investigated the effect of exposure for controlling a computer-based presentation and found that there is a learning effect of gestural control in that participants were significantly faster at generating intuitive mappings as they gained exposure with the system. The two Bayesian analyses were in the form of Bayesian multinomial logistic regression models where intuitive gesture choice was predicted based on the contextual task and either expertise or exposure. The Bayesian analyses generated posterior predictive probabilities for all combinations of task, expertise level, and exposure level and showed that gesture choice can be predicted to some degree. This work provides further insights into how 3D gestural input systems should be designed and how Bayesian statistics can be used to model human behavior

Examining the Impact of Design Features of Electronic Health Records Patient Portals on the Usability and Information Communication for Shared Decision Making

The use of the Electronic Health Records (EHR) patient portal has been shown to be effective in generating positive outcomes in patients’ healthcare, improving patient engagement and patient-provider communication. Government legislation also required proof of its meaningful use among patients by healthcare providers. Typical patient portals also include features such as health information and patient education materials. However, little research has examined the specific use of patient portals related to individuals with specific diseases such as inflammatory bowel diseases (IBDs). IBDs are life-long, not curable, chronic diseases that can impact the whole population. Individuals with IBDs may have higher needs to acquire health information from their EHR portals to properly self-manage their health conditions. The research aims of the present dissertation are to understand the online health information-seeking behaviors of a target group (IBDs) of patients, the use of EHR patient portals, and the impact of design features of EHR patient portals on the usability and information communication for shared decision making. Through this dissertation, I conducted four studies to address the above research aims. First, I identified how individuals with inflammatory bowel disease (IBD) used the internet for health information seeking, the factors impacting their use of the internet to obtain health information, and how they used the internet for health-related tasks. The purpose of this study is to get a general understanding of the online health information-seeking behaviors and to guide the study of health information presentation of EHR portals in the following research. Second, I examined what factors influenced an EHR patient portal user to believe that the portal is a valuable part of their health care. This part of the dissertation aimed to reveal the critical design factors that help design an EHR portal perceived as valuable in managing health. Third, I looked at how patients used EHR patient portals, what features of the portals facilitated their use and encouraged Shared Decision Making (SDM) and engagement in health management and what features acted as barriers to SDM and their engagement in health management. This part of my dissertation focused on a broad understanding of EHR portals usage by introducing more specific factors such as features of EHR portals. Fourth, I conducted an eye-tracking study to examine how information presentation methods and chatbots impact the use and effect of patient portals. This part of my dissertation built on the other studies within my dissertation and deepened the understanding of the influence of different EHR portal designs on their effectiveness and people’s willingness to participate in SDM. The results of this dissertation contribute to the literature of understanding the information-seeking behaviors of IBD patients and the use of portals, as well as the design considerations of how to make a suitable EHR portal to support the information-seeking needs of IBD patients. The results of this dissertation can be used to guide building proper patient education materials to support their health information needs of their specific health condition, especially for individuals with chronic diseases that require a certain amount of self-management. Meanwhile, examining artificial intelligence (AI) based chatbots use in EHR portals reveals a potential path of AI use in healthcare, such as information acquisition and patient education. Designing good usable EHR may also facilitate the process of informing patients of the advantages and disadvantages of treatment plans for their disease and, therefore, may increase their willingness to participate in SDM