Research

Patient-Generated Data
Data generated by patients outside clinical settings carries immense potential in informing decisions. However, the current ecosystem of tools does not help realize the potential of this data. I study the use of this data and design data interfaces to support analysis, interpretation, and decision-making in data-driven care of chronic health conditions. I draw from theories of sensemaking and techniques from information visualization to characterize practices of patients and clinicians and translate those practices to interface features.

Health Data, AI, and Decision-Support
Despite millions of dollars being spent in developing artificial intelligence capabilities, evidence shows that AI algorithms and systems that are successful in the lab often fail when deployed in the field - hospitals, and clinics. AI in its current state has been resulting in suboptimal experiences and results, which can be attributed to the lack of focus on the different types of users of AI, the contexts in which AI is embedded, and the consequences of deploying AI. Without a holistic understanding of the users of AI, the algorithms, the contexts in which the algorithms are used, and the consequences that they have or may have, AI systems will not achieve their potential and the results of AI deployment will remain suboptimal. I am now studying human interaction with artificial intelligence capabilities in healthcare technology, such as clinical decision-support tools for diagnostics, and patient-facing automated therapy systems to improve how healthcare AI systems are conceptualized, and designed. In a recent study with endocrinologists, my team designed and studied clinician-facing interfaces to augment data-driven decision-making for the care of T1D.

Context and Context-Awareness in Health
To support patients with chronic health conditions, it is important to understand the contexts in which self-care activities are performed and overall, the role of contextual factors in affecting health-related behaviors. I study the lived experiences of people to understand how context can be better utilized in interventions, such as just-in-time interventions, for assisting users in health-related activities. Specifically, I have studied the influence of context on the management of type 1 diabetes and on planning for physical activity. I am also interested in studying practices of designers who create context-based applications using health data.

Self-Reporting and EMA
Many researchers rely on self-reported inputs from their participants to get data. However, self-reporting remains a burdensome activity despite improvements in technology. I have collaborated with several colleagues to explore the use of situated self-reporting devices as compared to mobile phones for self-reporting. We have observed that mobile phones complement the use of stand-alone devices for self-reporting. I am interested in further assessing multi-device set-ups to improve self-reporting technology and experience.