Discovery Park Undergraduate Research Internship Program

"Empirical Prediction Modeling for Time to Hospital Readmission"

About the Project

Project Time & Type:
Fall 2015 - DURI
Research area(s):
Medical Informatics
Project Description:
It is common for patients in the United States to be readmitted to acute care hospitals after a short amount of time post hospital discharge. Hospital readmissions can serve as an important marker of poor health care quality and efficiency. To lower readmission rates, the U.S. Center for Medicaid and Medicare Services (CMS) began publicly reporting 30-day risk-standardized readmission rates for health failure (HF), acute myocardial infarction (MI), and pneumonia. These measures are part of a federal strategy to provide incentives to improve quality of care by reducing preventable readmissions. A bigger challenge faced by the U.S. healthcare system is the skyrocketing spending. The majority of the spending is incurred by providing care in acute care hospitals. While curbing the health care spending, it is important to maintain the quality of care provided. Hence, how to efficiently plan care transitions (e.g., from acute care to long-term care) is intensively studied in recent years. Critical to the development of effective programs to reduce readmissions and improve care transitions is an understanding of the influential factors causing the readmissions and the timing and diagnoses associated with readmission events. Our objective in this project is to develop accurate and robust empirical predictive models for time to the readmissions.
Expected Student Contributions:
health utilization data processing, survival modeling and analysis
Related Website(s):
https://www.hcup-us.ahrq.gov/.
Desired Qualifications:
Junior students; GPA >= 3.5; has some familiarity with R or SAS
Estimated Weekly Hours:
10
Department awards independent research credits for this project?
Yes, 3 credit hours

Professor in Charge

Name:
Kong, Nan
Deptartment/College:
biomedical engineering

Student Supervisor

Name:
Nan Kong
Title:
Associate Professor

Cooperating Faculty

Name:
Mark Ward
Deptartment/College:
Statistics