Research Project Title:
A Cause-Specific Risk-Prediction Model for Readmission in Skilled Nursing Facilities
abstract:There are approximately 15,000 skilled nursing facilities (SNF) nationwide participating in the Medicare and Medicaid programs. Hospitals discharge more Medicare beneficiaries to skilled nursing facilities (SNFs) than to any other post-acute care setting. Medicare patients discharged to a SNF have a 25% likelihood of readmission within 30 days. Hospital readmissions not only have the potential for negative physical, emotional, and psychological impacts on individuals in skilled nursing care, but also cost the Medicare program billions of dollars. The implementation of telehealth systems, which allows clinicians to remotely monitor patients and provide consultations, shows significant potential in reducing readmissions. This project seeks to develop a risk prediction model that aids in identifying patients at the skilled nursing facilities who would benefit from the use of these electronic intensive care unit systems. Giving more medical attention to these individuals will thereby reduce the occurrence of readmissions in skilled nursing facilities.
"SuperUROP offers me the chance to do advanced research and connect with those who will help me make that research more impactful. My background in interning at the Fred Hutch, SMART, and Philips, as well as participating in UROP in CSAIL, have equipped me with the experience to make the most of this opportunity. As a double major in Courses 6-7 and 18, I am excited to apply my skills in computer science and mathematics toward my interest in the medical field!"