Abstract TP392: Evaluating Rehabilitation Discharge Decision Indicators Among Hospitals Participating in Michigan’s Ongoing Stroke Registry to Accelerate Improvement of Care

Abstract

Background: There is considerable patient and hospital level variation in the selection of rehabilitation settings at the time of hospital discharge and contributing factors in decision making for post-acute care are poorly defined. Objective: Describe variability in frequency of discharge destination (acute, sub-acute, other facility type) of stroke patients to rehabilitation facilities across hospitals participating in Michigan’s Ongoing Stroke Registry to Accelerate Improvement of Care (MOSAIC), a statewide Coverdell stroke registry. Sample: Patients discharged from hospitals participating in MOSAIC’s hospital inventory between January 1- December 31, 2017 with clinical diagnosis of Ischemic or Hemorrhagic Stroke (N=3,129). Methods: Multinomial logistic regression analysis conducted to quantify associations between patient and hospital level factors related to discharge destination. Discharge destination was utilized as the outcome variable with all other factors (age, gender, race, insurance, functional status- admission/discharge, stroke type, hospital size, and stroke volume) as indicator variables. Results: From the model, 4 variables indicated significance with discharge destination. Patients suffering a hemorrhagic stroke were less likely to be discharge to acute or sub-acute facilities. Patients who were female, unable to ambulate at discharge, or of increased age (60+ years) were more likely to be discharged to a sub-acute facility. As such, hospitals with small bed sizes were more likely to discharge patients to sub-acute facilities than any other destination. Conclusions: Patient and hospital-based factors may predict discharge destination, with hospital bed size, stroke type, and discharge ambulatory status showing the strongest associations. Knowing major indicators related to decision-making processes, hospitals can reduce variability in decision making and improve quality of life outcomes for stroke patients.