Identifying High Risk Patients for Hospital Readmission

Authors

Ethan Graham, Southern Methodist UniversityFollow
Asha Saxena, Southern Methodist UniversityFollow
Heather Kirby, Frederick Regional Health SystemFollow

Abstract

The Affordable Care Act (ACA), passed in 2010, set forth a framework for healthcare providers to have a vested interest in better patient outcomes and to reduce the Total Cost of Care (TCOC) for patients. A large portion of TCOC comes from patients who make multiple unscheduled hospital visits for the same underlying pathology: a hospital readmission. In this paper, we tackle the difficulty of identifying risk markers for diabetes patients’ hospital readmission. Using data from the Health Facts Database, we use logistic regression and support vector machines to identify the risk that a diabetes patient has of a hospital readmission.

Recommended Citation

Graham, Ethan; Saxena, Asha; and Kirby, Heather (2019) "Identifying High Risk Patients for Hospital Readmission," SMU Data Science Review: Vol. 2: No. 1, Article 22.
Available at: https://scholar.smu.edu/datasciencereview/vol2/iss1/22

Creative Commons License

This work is licensed under a Creative Commons Attribution-Noncommercial 4.0 License