A Cost Prediction Tool for Cardiac Care in Rural, Urban, or Academic Medical Centers

Cardiac events are among the leading causes of death in the United States and cardiac healthcare costs continue to rise. However, preventive care is not accessible to everyone in this country. Many counties lack cardiologists, and limited healthcare resources contribute to variations in treatments and patient outcomes. Predictive models that estimate lengths of stay (LOS) and patient outcomes across different healthcare settings, including 30-day readmissions, provide valuable forecasts for patients, healthcare administrators, and insurance providers. Understanding these variations through predictive modeling enables healthcare professionals to make data-driven improvements to cardiac treatments. LOS is a key metric used to assess hospital costs and compare different hospital types. High readmission rates are also considered, as they often indicate longer LOS. The predictive model developed for this Praxis analyzes patient cardiac outcomes at academic medical centers, rural hospitals (including federally qualified health centers [FQHCs]), and urban hospitals. The model serves as a tool to determine whether total hospital charges are lower in rural hospitals compared to urban hospitals and academic medical centers (AMCs). The goal is for administrators, patients, and health insurance providers to use the model to compare cost-effective health outcomes.

Author

• Baust, EIleen Faye Keeney

Language

• en

Keyword

• HCUP Data
• Lasso
• Predictive Model
• Length of Stay
• Cardiac Events
• Readmission Rates

Date created

• 2025

Type of Work

• Dissertation

License

• All rights reserved

Rights statement

• In Copyright

GW Unit

• Engineering Management

Degree

• D.Engr.

Advisor

• Schlegelmilch, Barret W
• Heine, Karen

Committee Member(s)

• Small, Stanley

Persistent URL

•  https://scholarspace.library.gwu.edu/etd/00000091f