This award will contribute to the advancement of national health, prosperity and welfare by studying transitions from inpatient hospital units to skilled nursing facilities (SNFs). SNFs are a promising, cost-effective care alternative to expensive inpatient hospital rehabilitation, but poorly coordinated transitions can lead to poor health outcomes and higher costs via hospital readmissions. This project develops analytical methods to improve care transitions to SNFs. The research outcomes will help support guidelines for transfer decisions from hospitals to SNFs and will also be useful in guiding transitions between other hospital units. These guidelines have the potential to reduce cost and save lives. Collaboration with physicians and health services researchers will enable this project to achieve real, beneficial improvements in clinical care. The project seeks to increase the diversity in STEM through outreach, teaching, and dissemination and includes an outreach program to undergraduates from under-represented backgrounds, mentorship of PhD students, and integration of new methods into courses.
This project combines a data-driven approach based on observational studies with a stochastic decision-theoretic approach to determine patient transfer strategies that minimize readmissions. The sequential decision-making framework is based on a finite horizon, arm acquiring, and non independent and identically (iid) Restless Multiarmed Bandit (RMAB) problem. Theoretical and computational methods for the optimal control of arm acquiring and non iid RMABs are active areas of investigation. Readmission rates are estimated by individual SNF using large datasets from several states. The project will validate the methods developed via simulation of a large academic hospital and nearby SNFs and use the simulation to analyze policies for allocating patients to SNFs with respect to utilization, readmissions, and other costs of care.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.