Wei Liu, PhD

Data Scientist, ChristianaCare

Dr. Wei Liu is a data scientist at ChristianaCare, Delaware, one of the largest health systems nationwide. She is leading the enterprise’s advance analytic efforts with a focus on developing data mining and machine learning models for patient risk stratification, clinical decision support, population health management, and health equity evaluation. Prior to joining Christiana Care, Wei worked as a Senior Data Analyst at Children’s Hospital of Philadelphia (CHOP), where she conducted advanced data analytics to support decision making of hospital operation and patient flow, and as an Industrial Engineer at MD Anderson Cancer Center focused on process optimization and quality improvement. Wei holds a Master of Science in Health Systems from Georgia Institute of Technology in 2009 and Ph.D. in Industrial Engineering from Purdue University in 2017. Wei has a great passion for promoting quality and efficiency in a healthcare system with a special interest in using data-driven approaches to support decision making.

Speaker Sessions

13. Get Ahead of Adverse Outcomes: Risk Prediction and Targeted Intervention for COVID-19 Patients

Level: Intermediate
Track: Population Health and Value-Based Care

With limited resources and rapidly growing COVID-19 patient volumes, accurate and rapid identification of individuals at the highest risk of hospitalization is a top priority. To identify and target interventions towards high-risk patients, ChristianaCare leveraged a cloud-based data platform and analytics applications, including data science tools and predictive analytics, to generate a risk score for each patient testing positive for COVID-19. Join this session to learn how up-to-date risk identification helped the organization make data-informed resource allocation decisions and enroll patients in the appropriate care management program, avoiding 61 hospital admissions and 7 intensive care unit admissions and avoiding $1.8 million in cost.

Participants will learn:

  • What risk prediction tools were used.
  • How up-to-date risk identification helped the organization make timely, data-informed decisions.
  • What interventions were used to avoid hospital and ICU admissions.

Watch HAS 21 Virtual On-Demand.

Replay HAS 21 Virtual

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