There is a need for improved self-monitoring data sharing between patients and clinicians because the types of data from self-monitoring–physical activity, sleep, food, etc.–provide great insights into individuals’ health management, diagnosis, prevention, and treatment plan.
Eun Kyoung Choe
Eun Kyoung Choe
Dr. Choe's primary research areas are in the fields of Human-Computer Interaction, Ubiquitous Computing, and Health Informatics. The overarching goal of her research is to empower individuals and help them make positive behavior changes through fully leveraging their personal data. Putting people at the center of the data flow, she examines three specific interactions between individuals and their personal data: (1) data collection, (2) data exploration, and (3) data sharing. Using human-centered design methods and mixed-methods approaches, she aims to understand people’s challenges and needs regarding how people interact with data. Based on the understanding, she designs, develops, and evaluates web and mobile applications, visualizations, and multi-modal interactions. Dr. Choe applies these approaches to various health contexts, such as improving patient-clinician communication and patient care; leveraging patient-generated data; helping individuals form healthy habits; designing low-burden self-monitoring tools; and providing feedback and visualizations for personal data insights.
To empower individuals and help them make positive behavior changes through fully leveraging their personal data.
Current Research Interests
- Human-Computer Interaction
- Ubiquitous Computing
- Health Informatics
- PhD, Information Science, University of Washington, 2014
- MS, Information Management and Systems, University of California Berkeley, 2008
- BS, Industrial Design, Korea Advanced Institute of Science and Technology (KAIST), 2005
- NSF CRII award
- NSF CAREER award
- Google Anita Borg Memorial Scholarship
Self-monitoring for older adults and surgical patients can be difficult but beneficial–increased awareness, reduced negative behaviors, goal setting. This research looks at whether combining manual and automatic tracking (semi-automated tracking) can promote user engagement while reducing user burden for the best data collection.