Date of Award
Doctor of Philosophy (PhD)
Carol K. Sieck
Loyola University Chicago
CORRELATION OF THE BOOST RISK STRATIFICATION TOOL AS A PREDICTOR OF UNPLANNED 30-DAY REAMDISSION IN ELDERLY PATIENTS
Risk stratification tools can identify patients at risk for 30-day readmission but available tools lack predictive strength. While physical, functional and social determinants of health have demonstrated an association with readmission, available risk stratification tools have been inconsistent in their use of variables to predict readmission. The Better Outcomes by Optimizing Safe Transitions (BOOST) 8 P's tool is a risk stratification tool developed by the Society of Hospital Medicine but has no published validation studies. The theoretical foundation used for this study was Wagner's Care Model that illustrates the interconnected nature of acute and preventive care needed by chronically ill patients over a lifetime. This quantitative study using secondary data to measure the degree to which the BOOST variables predict 30-day readmission. The sample included one year of hospitalized patients 65+ (n=6849) from a tertiary hospital in the Midwest. Univariate and multivariate logistic regression demonstrated that six of the eight variables in the BOOST risk stratification tool showed significant predictive strength, including the social variables of health literacy (p=.030), depression (p=.003) and isolation (p=.011). Other significant variables included problem medications (p=.001), physical limitations (p=<.001) and prior hospitalization (p=<.001). The BOOST risk stratification tool had limited predictive capability with a C-statistic of .631. This study was the first attempt to validate the BOOST 8 P's tool and to utilize nursing documentation within an electronic medical record to capture social determinants of health. Implications for nursing practice include the need for nurses to gain skills in using risk stratification tools to identify patients at risk for readmission to target preventive interventions including care coordination efforts. Future research should target variables, especially social factors of depression, health literacy and isolation to predict 30-day readmission, especially for the growing population of elderly patients with chronic illness.
Sieck, Carol K., "Correlation of the Boost Risk Stratification Tool as a Predictor of Unplanned 30-Day Readmission in Elderly Patients" (2017). Dissertations. 2855.
Copyright © 2017 Carol K. Sieck