Improving Patient Selection and Prioritization for Hospital at Home Through Predictive Modeling

Hospital at home is designed to offer patients hospital level care in the comfort of their own home. The process by which clinicians select eligible patients that are clinically and socially appropriate for this model of care requires labor-intensive manual chart reviews. We addressed this problem by providing a predictive model, web application, and data pipeline that produces an eligibility score based on a set of clinical and social factors that influence patients' success in the program. Providers used this predictive model to prioritize the order in which they perform chart reviews and patient screenings. Training performance area under the curve (AUC) was 0.77. Testing 'in production' had an AUC of 0.75. Admission criteria in training rapidly changed over the course of the study due to the novelty of the clinical model. The current algorithm successfully identified many inconsistencies in enrollment and has streamlined the process of patient identification.