A data-driven approach towards patient identification for telehealth programs

Telehealth provides an opportunity to reduce healthcare costs through remote patient monitoring, but is not appropriate for all individuals. Our goal was to identify the patients for whom telehealth has the greatest impact, as measured through cost savings and patient engagement. For prediction of cost savings, challenges included the high variability of medical costs and the effect of selection bias on the cost difference between intervention patients and controls. Using Medicare claims data, we computed cost savings by comparing each telehealth patient to a group of control patients who had similar healthcare resource utilization. These estimates were then used to train a predictive model using logistic regression. Filtering the patients based on the model resulted in an average cost savings of $10K in the group of patients with the highest healthcare utilization, an improvement over the current expected loss of $2K (without filtering). Groups of patients with lower healthcare utilization also showed improvement, though less pronounced. To identify highly engaged patients, we developed predictive models of telehealth compliance and of patient satisfaction. Performance of these models were generally poor, with an AUC ranging from 0.54 to 0.64.