Ryan Shaw

Ryan Shaw

Associate Professor in the School of Nursing

Prof. Shaw  is faculty at the Duke University Schools of Nursing and Medicine. His research focuses on how patient generated health data from wearable devices, mobile phones and other emerging technologies can be used to manage and improve health outcomes in patients with chronic illnesses. With these tools he engineers models of care delivery that capitalize on the growing digital health infrastructure of health systems and society.

His work is funded by the National Institutes of Health (NIH) and the Agency for Healthcare Research and Quality (AHRQ), among others. He teaches classes in health informatics and mentors students to become the next generation of health scientists and clinicians. 

Appointments and Affiliations

  • Associate Professor in the School of Nursing
  • Associate of the Duke Initiative for Science & Society
  • Member of Duke Center for Applied Genomics and Precision Medicine

Contact Information

  • Office Location: School of Nursing, 3152, Durham, NC 27710
  • Office Phone: (919) 684-9434
  • Email Address: ryan.shaw@duke.edu


  • Ph.D. Duke University School of Nursing, 2012

Research Interests

Digital health
Electronic Health Records (EHRs)
health informatics
chronic illness management

Courses Taught

  • GLHLTH 396: Connections in Global Health: Interdisciplinary Team Projects
  • GLHLTH 796: Connections in Global Health: Interdisciplinary Team Projects
  • NURSING 574: Directed Scholarship
  • NURSING 575: Independent Study
  • NURSING 715: Database Systems in Healthcare: Design, Management, and Connectivity
  • NURSING 725: Synthesis of Specialty Practice, Informatics
  • NURSING 902: Quantitative Research Designs
  • NURSING 921: Integrated Research Practicum
  • NURSING 922: Special Readings in School of Nursing
  • NURSING 975: DNP Scholarly Project

In the News

Representative Publications

  • Yang, Q; Hatch, D; Crowley, MJ; Lewinski, AA; Vaughn, J; Steinberg, D; Vorderstrasse, A; Jiang, M; Shaw, RJ, Digital Phenotyping Self-Monitoring Behaviors for Individuals With Type 2 Diabetes Mellitus: Observational Study Using Latent Class Growth Analysis., Jmir Mhealth and Uhealth, vol 8 no. 6 (2020) [10.2196/17730] [abs].