Abstract: A computer-implemented method for personalizing a care program for a telehealth platform includes displaying a sequence of questions on a display of a user device including a plurality of personal needs questions and a plurality of questions associated with a clinical survey, receiving responses from a user to each question in the sequence of questions via an input device of the user device, storing the responses in a memory of the user device, assigning a primary concern to the user based on a response to at least a first question in the sequence of questions, assigning a severity score to the user based on the responses to the clinical survey, using a segmentation model to assign a recommended program to the user based on the primary concern and the severity score of the user, and notifying the user of the recommended program.

Abstract: A system, method, and computer program product for passively informed mental health risk prediction. The system may include receiving mental health risk input signals from a blood glucometer and other devices. The mental health risk signals may include glucometer data, demographic data, and other data. The glucometer data for the subject may include at least one blood glucose value. The mental health risk input signals are input into a machine learning system. The machine learning system has been previously trained with mental health risk input signals and mental health status data for a plurality of subjects. The machine learning system outputs a prediction of mental health risk for the subject. The machine learning system may comprise a neural network.