Predictive variables of discharge referrals: a comprehensive analysis of social determinants of health and patient discharge disposition at an urban safety-net medical center
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Citation
Abstract
OBJECTIVE: The role of Social Determinants of Health (SDOH) has been highlighted recently as a potentially significant variable of consideration in the discharge planning process. The purpose of this study was to determine if SDOH factors are significant contributors to discharge referral decisions when analyzed with multiple other non-SDOH variables. METHODS: This study prospectively collected data from patients admitted to an urban safety-net hospital between February 2024 and January 2025, excluding those under 18, in the ICU, sedated, requiring advanced PPE, intoxicated, aggressive, or needing an interpreter. Priority was given to patients over 60, from racial or ethnic minority groups, or with cognitive impairments. Data on SDOH variables and as well as other variables of interest were extracted from the larger study. Data analysis involved stepwise regression models using R Studio, with Akaike information criteria (AIC) scores and receiver operating characteristic (ROC) plots used to evaluate model accuracy and predictive ability. RESULTS: Data from 540 patients were analyzed in this study with 252 patients (46.7%) referred to home and 288 patients (53.5%) referred to some post-acute care (PAC) facility. Patients were more likely to be referred to a PAC facility if they had lower mobility scores, had a longer length of hospitalization or a higher depression score. The model developed in this study was found to be accurate with the ability to predict discharge referral destination 84.5% of the time. CONCLUSIONS: Mobility and length of stay significantly increase a patient’s likelihood of being discharged to a PAC facility. SDOH variables were not found to be significant predictors based on the model developed in this study. Further research is needed to determine which SDOH variables could have an impact on the discharge decision making process.
Description
2025