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Reducing potentially preventable readmissions has been identified as animportant issue for decreasing Medicare costs and improving quality of careprovided by hospitals. Based on previous research by medical professionals,preventable readmissions are caused by such factors as flawed patientdischarging process, inadequate follow-ups after discharging, and noncomplianceof patients on discharging and follow up instructions. It is also found thatthe risk of preventable readmission also may relate to some patient'scharacteristics, such as age, health condition, diagnosis, and even treatmentspecialty. In this study, using both general demographic information andindividual past history of readmission records, we develop a risk predictionmodel based on hierarchical nonlinear mixed effect framework to extractsignificant prognostic factors associated with patient risk of 30-dayreadmission. The effectiveness of our proposed approach is validated based on areal dataset from four VA facilities in the State of Michigan. Simultaneouslyexplaining both patient and population based variations of readmission process,such an accurate model can be used to recognize patients with high likelihoodof discharging non-compliances, and then targeted post-care actions can bedesigned to reduce further rehospitalization.

Predicting patient risk of readmission with frailty models in the Department of Veteran Affairs