The HUB for Excellence in eHealth Research (or “eHealth HUB”) brings together School of Nursing researchers, trainees and our transdisciplinary collaborators who are harnessing novel technologies and data science to improve health care and health outcomes. The goals of the hub are to foster collaboration, methodological innovation, and the inclusion of patients, providers and consumers in the co-creation of eHealth solutions to maximize their benefits and reduce their risks. The eHealth Hub offers important infrastructure support for technology-enabled research and data science that is grounded in behavioral theories and guided by principles of team science, user-centered design and design-justice.
Interactive Health Technologies Core
Lora Burke, PhD, RN, FAAN, Core Facilitator
Jamie Zelazny, PhD, RN, Core Facilitator
- The Interactive Health Technologies (IHT) Core focuses on the use of interactive technologies for the assessment and delivery of interventions applying the principles of user-centered design, design-justice, and strategies that support long-term engagement in the use of technology to support sustained behavior change and engagement in health care programs. The assessments include passive and or active measurement with EMA or wearable sensors. Delivery of interventions is typically accomplished via engagement with interactive smartphone apps, the web or telehealth targeting specific health and self-management behaviors.
Health Informatics and Data Science Core
Salah S. Al-Zaiti, PhD, RN, Core Facilitator
Theresa Koleck, PhD, RN, Core Facilitator
Young Ji Lee, PhD, MS, RN, Co- Facilitator
Fei Zhang PhD, Co- Facilitator
- The Health Informatics and Data Science (HIDS) Core focuses on the development and use of methods and technologies to acquire, process, and study data and discover and manage new knowledge relating to health and disease in individuals, groups, families, or communities. Examples of research and training content related to this core include data-driven research and discovery; artificial intelligence, machine learning, and predictive modeling; clinical data mining and secondary use of electronic health record; natural language processing; design of clinical decision support tools; and human computer interaction.