PROJECT SUMMARY Every year, about 11,000 home healthcare (HHC) agencies across the United States provide care to more than 5 million older adults. Currently, about one in three HHC patients are hospitalized or visit an emergency department (ED) and up to 40% of these events are preventable with appropriate and timely care. However, these numbers have not improved over the last decade, despite national and local quality improvement efforts. Recent advances in a subfield of data science—automated speech processing—have unlocked an untapped rich data stream that can improve risk identification by analyzing nurse-patient verbal communication. The proposed study brings together an interdisciplinary team of experts in home healthcare nursing, automated speech processing, natural language processing, and risk model development to explore whether automated speech processing can improve timely identification of patients at risk in home healthcare and potentially reduce their hospitalizations and ED visits. Specifically, the aims of this study are: Aim 1: Refine and finalize an automated speech processing system to identify hospitalization and ED visit risk factors in patient-nurse verbal communications. Aim 2: Explore to what extent data extracted from patient-nurse communications can improve risk prediction for hospitalizations and ED visits, when compared against the risk model based on electronic health record data only. This study will build a first-of-a-kind hospitalization and ED visit risk model that automatically incorporates data from patient-nurse verbal communication. In future work, this risk model can be integrated into home healthcare clinical workflows to trigger timely and personalized alerts about concerning patient trends, which will in turn activate appropriate and timely care to prevent avoidable hospitalizations and ED visits from HHC.