Hospital Readmissions Among Patients Experiencing Homelessness: An Electronic Health Record Data Study.

BACKGROUND: Population-level tracking of hospital use patterns with integrated care organizations in patients experiencing homelessness has been difficult. A California law implemented in 2019 (Senate Bill 1152) aimed to ensure safety for this population after discharge from the hospital by requiring additional documentation for patients experiencing homelessness, which provides an opportunity to evaluate hospital use by this population. METHODS: In a large integrated health system in California, patients experiencing homelessness were identified through documentation change requirements associated with this law and compared with a matched group from the general population. RESULTS: Patients experiencing homelessness had increased rates of hospital readmission after discharge compared to the general population matched on demographics and medical comorbidity in 2019 and 2020. Any address change in the prior year for patients was associated with increased odds of emergency department readmission. Patients experiencing homelessness, both enrolled in an integrated delivery system and not, were successfully identified as having higher readmission rates compared with their housed counterparts. CONCLUSION: Documentation of housing status following Senate Bill 1152 has enabled improved study of hospital use among those with housing instability. Understanding patterns of hospital use in this vulnerable group will help practitioners identify timely points of intervention for further social and health care support.

Identifying Predictors of Homelessness Among Adults in a Large Integrated Health System in Northern California.

Introduction Homelessness contributes to worsening health and increased health care costs. There is little published research that leverages rich electronic health record (EHR) data to predict future homelessness risk and inform interventions to address it. The authors' objective was to develop a model for predicting future homelessness using individual EHR and geographic data covariates. Methods This retrospective cohort study included 2,543,504 adult members (≥ 18 years old) from Kaiser Permanente Northern California and evaluated which covariates predicted a composite outcome of homelessness status (hospital discharge documentation of a homeless patient, medical diagnosis of homelessness, approved medical financial assistance application for homelessness, and/or "homeless/shelter" in address name). The predictors were measured in 2018-2019 and included prior diagnoses and demographic and geographic data. The outcome was measured in 2020. The cohort was split (70:30) into a derivation and validation set, and logistic regression was used to model the outcome. Results Homelessness prevalence was 0.35% in the overall sample. The final logistic regression model included 26 prior diagnoses, demographic, and geographic-level predictors. The regression model using the validation set had moderate sensitivity (80.4%) and specificity (83.2%) for predicting future cases of homelessness and achieved excellent classification properties (area under the curve of 0.891 [95% confidence interval = 0.884-0.897]). Discussion This prediction model can be used as an initial triage step to enhance screening and referral tools for identifying and addressing homelessness, which can improve health and reduce health care costs. Conclusions EHR data can be used to predict chance of homelessness at a population health level.