The Health Systems Dashboard helps states and health care leaders better understand their health systems and how they are performing, including a look at hospital readmission rates across six health conditions.
- Machine learning
- Natural language processing
- Data visualization
- Reliability testing
- Risk-adjustment models
- Human Services
Fei Xing’s work focuses on applying data science solutions to improve operations and support decision making for a variety of federal, state, and commercial clients.
Xing has worked on a range of projects in health, education, and nutrition. Currently, he is the project director for a study using natural language processing and machine learning to help the National Science Foundation revolutionize its occupation coding process for the Survey of Doctorate Recipients. Xing also led a range of tasks to develop, test, and maintain scientifically sound health quality measures and support their implementation in multiple quality programs for the Centers for Medicare & Medicaid Services. Specifically, he has expertise with reliability and validity testing and risk adjustment, and has supported more than 10 health quality measures—based on claims and electronic health records—that were endorsed by the National Quality Forum. As director of data science, Xing is responsible for staff development and team building, and he leads the Data Science team to provide data science solutions on projects, proposals, and business development efforts.
Before joining Mathematica in 2013, Xing worked on projects developing statistical models to identify hot spots of performance bottleneck and improve the user experience of supercomputers at the Oak Ridge National Laboratory. He holds a Ph.D. in mathematics from the University of Tennessee, Knoxville.
New Dashboard Shows Which Hospitals Have the Highest (and Lowest) Readmissions Across Health Systems