Building a Time-Series Model to Predict Hospitalization Risks in Home Health Care: Insights Into Development, Accuracy, and Fairness

Home health care (HHC) serves more than 5 million older adults annually in the United States, aiming to prevent unnecessary hospitalizations and emergency department (ED) visits. Despite efforts, up to 25% of HHC patients experience these adverse events. The underutilization of clinical notes, aggregated data approaches, and potential demographic biases have limited previous HHC risk prediction models. This study aimed to develop a time-series risk model to predict hospitalizations and ED visits in HHC patients, examine model performance over various prediction windows, identify top predictive variables and map them to data standards, and assess model fairness across demographic subgroups.