J. Li

Patient-provider communication has been crucial to cancer patients' survival after their cancer treatments. However, the research community and patients themselves often overlook the communication challenges after cancer treatments as they are overshadowed by the severity of the patient's illness and the variety and rarity of the cancer disease itself. Meanwhile, the recent technical advances in AI, especially in Large Language Models (LLMs) with versatile natural language interpretation and generation ability, demonstrate great potential to support communication in complex real-world medical situations. By interviewing six healthcare providers and eight cancer patients, our goal is to explore the providers' and patients' communication barriers in the post-cancer treatment recovery period, their expectations for future communication technologies, and the potential of AI technologies in this context. Our findings reveal several challenges in current patient-provider communication, including the knowledge and timing gaps between cancer patients and providers, their collaboration obstacles, and resource limitations. Moreover, based on providers' and patients' needs and expectations, we summarize a set of design implications for intelligent communication systems, especially with the power of LLMs. Our work sheds light on the design of future AI-powered systems for patient-provider communication under high-stake and high-uncertainty situations.

While aging adults frequently prefer to "age in place", their children can worry about their well-being, especially when they live at a distance. Many in-home systems are designed to monitor the real-time status of seniors at home and provide information to their adult children. However, we observed that the needs and concerns of both sides in the information sharing process are often not aligned. In this research, we examined the design of a system that mitigates the privacy needs of aging adults in light of the information desires of adult children. We apply an iterative process to design and evaluate a visualization of indoor location data and compare its benefits to displaying raw video from cameras. We elaborate on the tradeoffs surrounding privacy and awareness made by older adults and their children, and synthesize design criteria for designing a visualization system to manage these tensions and tradeoffs.

The rapid advancement of the Large Language Model (LLM) has created numerous potentials for integration with conversational assistants (CAs) assisting people in their daily tasks, particularly due to their extensive flexibility. However, users' real-world experiences interacting with these assistants remain unexplored. In this research, we chose cooking, a complex daily task, as a scenario to investigate people's successful and unsatisfactory experiences while receiving assistance from an LLM-based CA, Mango Mango. We discovered that participants value the system's ability to provide extensive information beyond the recipe, offer customized instructions based on context, and assist them in dynamically planning the task. However, they expect the system to be more adaptive to oral conversation and provide more suggestive responses to keep users actively involved. Recognizing that users began treating our LLM-CA as a personal assistant or even a partner rather than just a recipe-reading tool, we propose several design considerations for future development.