Xueping Li

Xueping Li Ph.D. is an associate professor in the Department of Industrial and Systems Engineering, director of the Ideation Laboratory (iLab) in the Tickle College of Engineering, and co-director of the Health Innovation Technology and Simulation (HITS) Lab at the University of Tennessee, Knoxville. His research focuses on health care and complex system modeling, simulation, and optimization. Born in Sichuan, China, Li received his master’s degree in computer science from Nankai University and his PhD in industrial engineering from Arizona State University. He joined UT in 2005 as a faculty member in the Department of Industrial Engineering.

Li’s interest in health care led to him to collaborate with Tami Wyatt, the Torchbearer Professor and Associate Dean of Research in UT’s College of Nursing, in 2006. Their joint research efforts laid the foundation for the HITS lab, which was formally established in 2011. This interdisciplinary laboratory brings together experts from across UT, including engineers, nurses, graphic designers, and social workers, to engage health care professionals in active learning and solve common health care problems through innovative techniques and solutions. In 2010, Li cofounded the iLab with Lee Martin, a professor of practice in the Department of Industrial and Systems Engineering, to provide students and faculty with a space where they can prototype and test ideas to see if they can be transformed into useful products.

Xueping Li

Xueping Li

Xueping Li

Li’s primary focus within the HITS lab is on the research and development of technologies and applications that promote learning among health care professionals and patient well-being. This work complements, and even overlaps, research conducted in the iLab, as several of the apps and sensors being developed in the HITS lab were first prototyped in the iLab.

One of these apps is the Simulated Electronic Fetal Monitoring (SEFM) app, which was designed to simulate fetal heart and contraction patterns in pregnant and birthing mothers. Students who use the app learn to recognize different fetal heartbeat patterns and monitor a fetus’ well-being during labor. One version of the app is available to download in Apple’s App Store, while another more robust in-house version is being tested at the Naval Medical Center in Portsmouth, Virginia.

Li is also working on another app that allows children with asthma to communicate with family members, health care providers, and school personnel regarding their asthma care and control needs. The JASMIN app – which stands for Just-in-Time Asthma Self-Management Intervention – contains a number of features that makes it easy for a child and his or her “care team” to track, self-manage, and interact with the child’s treatment plan, including alerts for when a child has not used his or her peak flow meter and heat maps that identify locations that contain a number of asthma triggers. Li and the HITS team recently received approval to conduct a pilot study of JASMIN with East Tennessee Children’s Hospital.

In addition, Li is developing an app called the Interactive Debriefing Application (IDA) that turns passive student observers into active learners, allowing them to participate in simulations by annotating live feed video. Following a simulation, the IDA allows instructors to aggregate comments and observed feedback and use that information to inform face-to-face debriefings. Li and the HITS team conducted focus groups at several universities during the spring 2018 semester to collect feedback on the app. The IDA has been disclosed to UTRF, and Li and the HITS team are speaking with a publishing company interested in licensing it.

Li’s research in the HITS lab is not solely focused on app development. He’s also using big data to build predictive models that can be used in a health care context. For example, Li is using data from electronic health records to develop a model that predicts which patients in a hospital setting are likely to get sepsis. In another project, Li is investigating ways to harness the power of the Internet of Things and machine learning algorithms for predictive maintenance.

UTRF staff have guided Li through the licensing process and provided seed funding for research projects, and Li credits UTRF with helping him and his HITS collaborators bring their technologies to life saying:

“They’re a tremendous help.”