  Reducing potentially preventable readmissions has been identified as an
important issue for decreasing Medicare costs and improving quality of care
provided by hospitals. Based on previous research by medical professionals,
preventable readmissions are caused by such factors as flawed patient
discharging process, inadequate follow-ups after discharging, and noncompliance
of patients on discharging and follow up instructions. It is also found that
the risk of preventable readmission also may relate to some patient's
characteristics, such as age, health condition, diagnosis, and even treatment
specialty. In this study, using both general demographic information and
individual past history of readmission records, we develop a risk prediction
model based on hierarchical nonlinear mixed effect framework to extract
significant prognostic factors associated with patient risk of 30-day
readmission. The effectiveness of our proposed approach is validated based on a
real dataset from four VA facilities in the State of Michigan. Simultaneously
explaining both patient and population based variations of readmission process,
such an accurate model can be used to recognize patients with high likelihood
of discharging non-compliances, and then targeted post-care actions can be
designed to reduce further rehospitalization.
