The objective of the project is to create scalable solutions to unlock previously underutilized nursing, physical therapy, occupational therapy and speech and language pathology (RN/PT/OT/SLP) data with routinely collected medical data and integrate it with AI technology. This project focus on RN/PT/OT/SLP data, which is updated more frequently and regularly than data from physician providers in both inpatient and outpatient settings. Specifically, this project will target two common, costly patient populations from across the lifespan whose care needs require multiple disciplines: 1) adult patient falls with trauma/injury requiring hospital admission, and 2) the transition from the neonatal intensive care unit (NICU) to home with preterm or medically complex neonates. The team will use human-curated AI to enable access to the data and innovate on the summarization of the complex episodes of care. The project performance sites include one urban hospital serving a diverse metropolitan population and two hospital systems in smaller cities, each with a large rural catchment area, which will provide racial and ethnic diversity, as well as rural representation in our enclave data. The multidisciplinary LLMs will be released to the world at the end of this project to drive future innovation.