Using electronic health record metadata to predict housing instability amongst veterans.

ABSTRACT: Housing instability is considered a significant life stressor and preemptive screening should be applied to identify those at risk for homelessness as early as possible so that they can be targeted for specialized care. We developed models to classify patient outcomes for an established VA Homelessness Screening Clinical Reminder (HSCR), which identifies housing instability, in the two months prior to its administration. Logistic Regression and Random Forest models were fit to classify responses using the last 18 months of document activity. We measure concentration of risk across stratifications of predicted probability and observe an enriched likelihood of finding confirmed false negative responses from veterans with diagnosed housing instability. Positive responses were 34 times more li

SUBMITTER: Zamora-Resendiz R 

PROVIDER: S-EPMC10796937 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature