  Hospital inpatient care costs is the largest component of health care
expenditures in the US. At the same time, the number of non-hospital
rehabilitative settings, such as skilled nursing facilities (SNFs), has
increased. Lower costs and increased availability have made SNFs and other
non-hospital rehabilitation settings a promising care alternative to
hospitalization. To maximize their benefits, transitions to SNFs require
special attention, since poorly coordinated transitions can lead to worse
outcomes and higher costs via unnecessary hospital readmissions. This study
presents a framework to improve care transitions based on the premise that
certain SNFs may provide better care for some patients. We estimate readmission
rates by SNF and patient types using observational data from a tertiary
teaching hospital and nearby SNFs. We then analyze and solve a stochastic model
optimizing patient transfer decisions to minimize readmissions. Our model
accounts for patient discharge patterns and SNF capacity availability. We
provide conditions for when an easy-to-use myopic policy, which assigns
discharged patients to the SNF with the lowest readmission rate that is
available, is optimal. We also show when an optimal policy has a threshold-like
structure. Using estimated readmission rates, we compare the performance of the
myopic policy and a proposed policy that depends on the discharge process,
readmission rates, and future SNF availability. We evaluate when the myopic
policy may be beneficial to use and when the proposed transfer heuristic
provides a better alternative. Otherwise, we contend that using a stochastic
optimization model for guiding transfer decisions may help reduce readmissions.
