Maher Khelifi

Hospitalized patients of the 21st century are encouraged to actively engage in their care, manage their safety, make medical decisions, and monitor the quality of their treatments. However, engaged hospitalized patients face a dilemma. The complexity of their care makes their engagement more important yet harder to achieve. Patients with complex health problems are cognitively and physically impaired because of pain, stress, and medications. At the same time, the information related to their health situation is more abundant and more complex. Thus, hospitalized patients face an engagement gap that grows deeper with the complexity of their health problems. Artificial intelligence (AI) agents, technologies that automate information processing, could be a promising solution. Yet, we know little about how AI agents could support patients in hospital settings. In this thesis, first, I start by defining technological opportunities, especially AI applications, to support patient and information needs in hospital settings. I propose a new user-centered research method “Muse cards”. The method aims to inspire patients and their family caregiver to disrupt their hospital technologies with new designs that would accommodate their evolving roles in hospital settings. Second, I focus on the patient-clinician conversation, a core source of information in hospital settings. I report the factors that define the importance of verbally communicated information for patients, from the patients’ perspective and from the clinicians’ perspective. Third, I report the results of testing NURI, an AI agent to support hospitalized patients in understanding medical conversations with their clinicians. I report the perception of its usefulness and acceptance form the patients, caregivers, and clinician’s perspective. My work contributes to human computer interaction research a new toolkit to help users disrupt their attachment to existing technologies with new innovative ideas. Moreover, I provide design guidelines to implement AI agents in the hospital settings to support patients and their family caregivers. Furthermore, my work contributes to clinical speech processing research by providing an annotation framework to capture important information for patients’ use from the patient perspective and from the clinicians’ perspective.